Handling controller and node failure scenarios during data collection

ABSTRACT

Systems, methods, and computer-readable media for handling failure scenarios during data collection and analysis for assurance. In some examples, a system can obtain a logical model of a network and, based on the logical model, identify a plurality of controllers on the network and a plurality of nodes on a fabric of the network. The system can probe the plurality of controllers and plurality of nodes for respective status information, including respective reachability information, respective login diagnostics information, and/or respective software information. Based on the respective status information, the system can determine conditions at the plurality of controllers and the plurality of nodes and define one or more assurance operations based on the conditions at the plurality of controllers and the plurality of nodes. The system can then perform the one or more assurance operations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 62/520,675, filed on Jun. 16, 2017, entitled “HANDLING FAILURE SCENARIOS DURING DATA COLLECTION”, the contents of which are hereby expressly incorporated by reference in its entirety.

TECHNICAL FIELD

The present technology pertains to network configuration and troubleshooting, and more specifically to handling failure scenarios during data collection and assurance in a network.

BACKGROUND

Computer networks are becoming increasingly complex, often involving low level as well as high level configurations at various layers of the network. For example, computer networks generally include numerous access policies, forwarding policies, routing policies, security policies, etc., which together define the overall behavior and operation of the network. Network operators have a wide array of configuration options for tailoring the network to the needs of the users. While the different configuration options available provide network operators a great degree of flexibility and control over the network, they also add to the complexity of the network. In many cases, the configuration process can become highly complex. Not surprisingly, the network configuration process is increasingly error prone. In addition, troubleshooting errors in a highly complex network can be extremely difficult. The process of identifying the root cause of undesired behavior and handling failures in the network can be a daunting task.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate example network environments;

FIG. 2A illustrates an example object model for a network;

FIG. 2B illustrates an example object model for a tenant object in the example object model from FIG. 2A;

FIG. 2C illustrates an example association of various objects in the example object model from FIG. 2A;

FIG. 2D illustrates a schematic diagram of example models for implementing the example object model from FIG. 2A;

FIG. 3A illustrates an example assurance appliance system;

FIG. 3B illustrates an example system diagram for network assurance;

FIG. 4A illustrates a diagram of a first example approach for constructing a logical model of a network;

FIG. 4B illustrates a diagram of a second example approach for constructing a logical model of a network;

FIG. 4C illustrates an example diagram for constructing device-specific logical models based on a logical model of a network;

FIG. 5A illustrates a schematic diagram of an example policy analyzer;

FIG. 5B illustrates an equivalency diagram for different network models;

FIG. 5C illustrates an example architecture for identifying conflict rules;

FIG. 6A illustrates a first example conflict Reduced Ordered Binary Decision Diagram (ROBDD);

FIG. 6B illustrates a second example conflict ROBDD;

FIG. 6C illustrates the example conflict ROBDD of FIG. 6B with an added rule;

FIG. 7A illustrates an example method for network assurance;

FIG. 7B illustrates a first example method for handling error scenarios during data collection and analysis;

FIG. 7C illustrates a second example method for handling error scenarios during data collection and analysis;

FIG. 8 illustrates an example network device; and

FIG. 9 illustrates an example computing device.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments.

Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification.

Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.

Overview

Software-defined networks (SDNs), such as application-centric infrastructure (ACI) networks, can be managed and configured from one or more centralized network elements, such as application policy infrastructure controllers (APICs) in an ACI network or network managers in other SDN networks. A network operator can define various configurations, objects, rules, etc., for the SDN network, which can be implemented by the one or more centralized network elements. The configuration information provided by the network operator can reflect the network operator's intent for the SDN network, meaning, how the network operator intends for the SDN network and its components to operate. Such user intents can be programmatically encapsulated in logical models stored at the centralized network elements. The logical models can represent the user intents and reflect the configuration of the SDN network. For example, the logical models can represent the object and policy universe (e.g., endpoints, tenants, endpoint groups, networks or contexts, application profiles, services, domains, policies, etc.) as defined for the particular SDN network by the user intents and/or centralized network elements.

In many cases, various nodes and/or controllers in a network may contain respective information or representations of the network and network state. For example, different controllers may store different logical models of the network and each node in the fabric of the network may contain its own configuration model for the network. The approaches set forth herein can perform data collection and analysis for various controllers and nodes in the network, to obtain network and device models and perform network assurance checks based on the network and device models. These modeling approaches can provide significant insight, foresight, and visibility into the network. The approaches set forth herein can also provide strategies for handling failure scenarios during data collection and analysis, including controller and node failure scenarios. For example, the approaches herein can detect a failure scenario during data collection and/or analysis, and provide an actionable event or response to the failure scenario.

Disclosed herein are systems, methods, and computer-readable media for handling failure scenarios during data collection and assurance in a network. In some examples, a system can probe a plurality of controllers in a network, such as a software-defined network (SDN), for respective status information. The status information can include a reachability status, a software version, a controller version, a current connectivity, a configuration status, etc. Based on the respective status information, the system can identify a number of controllers from the plurality of controllers having a reachable status and a supported controller version. The system can determine whether the number of controllers having the reachable status and the supported controller version form a quorum. When the number of controllers forms a quorum, the system can collect a respective logical model of the network from each controller in the quorum. Based on the respective logical model collected from each controller in the quorum, the system can determine respective node information for each node in a fabric of the network and, based on the respective node information, generate a list of nodes in the fabric to include in one or more assurance analysis operations.

The one or more assurance analysis operations can include, for example, data collection and modeling operations, assurance and equivalence operations, validity operations, analysis operations, etc. The system can make failure decisions during the one or more assurance operations as well as data collection and determination operations performed. The failure decisions can include, for example, collection, analysis, and failure decisions, and can be based on the respective status information, the various determinations, the list, etc.

EXAMPLE EMBODIMENTS

The disclosed technology addresses the need in the art for network modeling and assurance operations and efficient and effective failure decisions during collection and analysis. The present technology involves system, methods, and computer-readable media for handling failure scenarios during data collection and analysis for network assurance. The present technology will be described in the following disclosure as follows. The discussion begins with an introductory discussion of network assurance and a description of example computing environments, as illustrated in FIGS. 1A and 1B. A discussion of network models for network assurance, as shown in FIGS. 2A through 2D, as well as network modeling, assurance and error handling, as shown in FIGS. 3A-C, 4A-B, 5A-C, 6A-C, and 7A-C will then follow. The discussion concludes with a description of example network and computing devices, as illustrated in FIGS. 8 and 9, including example hardware components suitable for hosting software applications and performing computing operations.

The disclosure now turns to an introductory discussion of network assurance.

Network assurance is the guarantee or determination that the network is behaving as intended by the network operator and has been configured properly (e.g., the network is doing what it is intended to do). Intent can encompass various network operations, such as bridging, routing, security, service chaining, endpoints, compliance, QoS (Quality of Service), audits, etc. Intent can be embodied in one or more policies, settings, configurations, etc., defined for the network and individual network elements (e.g., switches, routers, applications, resources, etc.). However, often times, the configurations, policies, etc., defined by a network operator are incorrect or not accurately reflected in the actual behavior of the network. For example, a network operator specifies a configuration A for one or more types of traffic but later finds out that the network is actually applying configuration B to that traffic or otherwise processing that traffic in a manner that is inconsistent with configuration A. This can be a result of many different causes, such as hardware errors, software bugs, varying priorities, configuration conflicts, misconfiguration of one or more settings, improper rule rendering by devices, unexpected errors or events, software upgrades, configuration changes, failures, etc. As another example, a network operator implements configuration C but one or more other configurations result in the network behaving in a manner that is inconsistent with the intent reflected by the implementation of configuration C. For example, such a situation can result when configuration C conflicts with other configurations in the network.

The approaches herein can provide network assurance by modeling various aspects of the network and/or performing consistency checks as well as other network assurance checks. The network assurance approaches herein can be implemented in various types of networks, including a private network, such as a local area network (LAN); an enterprise network; a standalone or traditional network, such as a data center network; a network including a physical or underlay layer and a logical or overlay layer, such as a VXLAN or software-defined network (SDN) (e.g., Application Centric Infrastructure (ACI) or VMware NSX networks); etc.

Network models can be constructed for a network and implemented for network assurance. A network model can provide a representation of one or more aspects of a network, including, without limitation the network's policies, configurations, requirements, security, routing, topology, applications, hardware, filters, contracts, access control lists, infrastructure, etc. As will be further explained below, different types of models can be generated for a network.

Such models can be implemented to ensure that the behavior of the network will be consistent (or is consistent) with the intended behavior reflected through specific configurations (e.g., policies, settings, definitions, etc.) implemented by the network operator. Unlike traditional network monitoring, which involves sending and analyzing data packets and observing network behavior, network assurance can be performed through modeling without necessarily ingesting packet data or monitoring traffic or network behavior. This can result in foresight, insight, and hindsight: problems can be prevented before they occur, identified when they occur, and fixed immediately after they occur.

Thus, network assurance can involve modeling properties of the network to deterministically predict the behavior of the network. The network can be determined to be healthy if the model(s) indicate proper behavior (e.g., no inconsistencies, conflicts, errors, etc.). The network can be determined to be functional, but not fully healthy, if the modeling indicates proper behavior but some inconsistencies. The network can be determined to be non-functional and not healthy if the modeling indicates improper behavior and errors. If inconsistencies or errors are detected by the modeling, a detailed analysis of the corresponding model(s) can allow one or more underlying or root problems to be identified with great accuracy.

The modeling can consume numerous types of smart events which model a large amount of behavioral aspects of the network. Smart events can impact various aspects of the network, such as underlay services, overlay services, tenant connectivity, tenant security, tenant end point (EP) mobility, tenant policy, tenant routing, resources, etc.

Having described various aspects of network assurance, the disclosure now turns to a discussion of example network environments for network assurance.

FIG. 1A illustrates a diagram of an example Network Environment 100, such as a data center. The Network Environment 100 can include a Fabric 120 which can represent the physical layer or infrastructure (e.g., underlay) of the Network Environment 100. Fabric 120 can include Spines 102 (e.g., spine routers or switches) and Leafs 104 (e.g., leaf routers or switches) which can be interconnected for routing or switching traffic in the Fabric 120. Spines 102 can interconnect Leafs 104 in the Fabric 120, and Leafs 104 can connect the Fabric 120 to an overlay or logical portion of the Network Environment 100, which can include application services, servers, virtual machines, containers, endpoints, etc. Thus, network connectivity in the Fabric 120 can flow from Spines 102 to Leafs 104, and vice versa. The interconnections between Leafs 104 and Spines 102 can be redundant (e.g., multiple interconnections) to avoid a failure in routing. In some embodiments, Leafs 104 and Spines 102 can be fully connected, such that any given Leaf is connected to each of the Spines 102, and any given Spine is connected to each of the Leafs 104. Leafs 104 can be, for example, top-of-rack (“ToR”) switches, aggregation switches, gateways, ingress and/or egress switches, provider edge devices, and/or any other type of routing or switching device.

Leafs 104 can be responsible for routing and/or bridging tenant or customer packets and applying network policies or rules. Network policies and rules can be driven by one or more Controllers 116, and/or implemented or enforced by one or more devices, such as Leafs 104. Leafs 104 can connect other elements to the Fabric 120. For example, Leafs 104 can connect Servers 106, Hypervisors 108, Virtual Machines (VMs) 110, Applications 112, Network Device 114, etc., with Fabric 120. Such elements can reside in one or more logical or virtual layers or networks, such as an overlay network. In some cases, Leafs 104 can encapsulate and decapsulate packets to and from such elements (e.g., Servers 106) in order to enable communications throughout Network Environment 100 and Fabric 120. Leafs 104 can also provide any other devices, services, tenants, or workloads with access to Fabric 120. In some cases, Servers 106 connected to Leafs 104 can similarly encapsulate and decapsulate packets to and from Leafs 104. For example, Servers 106 can include one or more virtual switches or routers or tunnel endpoints for tunneling packets between an overlay or logical layer hosted by, or connected to, Servers 106 and an underlay layer represented by Fabric 120 and accessed via Leafs 104.

Applications 112 can include software applications, services, containers, appliances, functions, service chains, etc. For example, Applications 112 can include a firewall, a database, a CDN server, an IDS/IPS, a deep packet inspection service, a message router, a virtual switch, etc. An application from Applications 112 can be distributed, chained, or hosted by multiple endpoints (e.g., Servers 106, VMs 110, etc.), or may run or execute entirely from a single endpoint.

VMs 110 can be virtual machines hosted by Hypervisors 108 or virtual machine managers running on Servers 106. VMs 110 can include workloads running on a guest operating system on a respective server. Hypervisors 108 can provide a layer of software, firmware, and/or hardware that creates, manages, and/or runs the VMs 110. Hypervisors 108 can allow VMs 110 to share hardware resources on Servers 106, and the hardware resources on Servers 106 to appear as multiple, separate hardware platforms. Moreover, Hypervisors 108 on Servers 106 can host one or more VMs 110.

In some cases, VMs 110 and/or Hypervisors 108 can be migrated to other Servers 106. Servers 106 can similarly be migrated to other locations in Network Environment 100. For example, a server connected to a specific leaf can be changed to connect to a different or additional leaf. Such configuration or deployment changes can involve modifications to settings, configurations and policies that are applied to the resources being migrated as well as other network components.

In some cases, one or more Servers 106, Hypervisors 108, and/or VMs 110 can represent or reside in a tenant or customer space. Tenant space can include workloads, services, applications, devices, networks, and/or resources that are associated with one or more clients or subscribers. Accordingly, traffic in Network Environment 100 can be routed based on specific tenant policies, spaces, agreements, configurations, etc. Moreover, addressing can vary between one or more tenants. In some configurations, tenant spaces can be divided into logical segments and/or networks and separated from logical segments and/or networks associated with other tenants. Addressing, policy, security and configuration information between tenants can be managed by Controllers 116, Servers 106, Leafs 104, etc.

Configurations in Network Environment 100 can be implemented at a logical level, a hardware level (e.g., physical), and/or both. For example, configurations can be implemented at a logical and/or hardware level based on endpoint or resource attributes, such as endpoint types and/or application groups or profiles, through a software-defined network (SDN) framework (e.g., Application-Centric Infrastructure (ACI) or VMWARE NSX). To illustrate, one or more administrators can define configurations at a logical level (e.g., application or software level) through Controllers 116, which can implement or propagate such configurations through Network Environment 100. In some examples, Controllers 116 can be Application Policy Infrastructure Controllers (APICs) in an ACI framework. In other examples, Controllers 116 can be one or more management components for associated with other SDN solutions, such as NSX Managers.

Such configurations can define rules, policies, priorities, protocols, attributes, objects, etc., for routing and/or classifying traffic in Network Environment 100. For example, such configurations can define attributes and objects for classifying and processing traffic based on Endpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains (BDs), virtual routing and forwarding instances (VRFs), tenants, priorities, firewall rules, etc. Other example network objects and configurations are further described below. Traffic policies and rules can be enforced based on tags, attributes, or other characteristics of the traffic, such as protocols associated with the traffic, EPGs associated with the traffic, SGs associated with the traffic, network address information associated with the traffic, etc. Such policies and rules can be enforced by one or more elements in Network Environment 100, such as Leafs 104, Servers 106, Hypervisors 108, Controllers 116, etc. As previously explained, Network Environment 100 can be configured according to one or more particular software-defined network (SDN) solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutions are briefly described below.

ACI can provide an application-centric or policy-based solution through scalable distributed enforcement. ACI supports integration of physical and virtual environments under a declarative configuration model for networks, servers, services, security, requirements, etc. For example, the ACI framework implements EPGs, which can include a collection of endpoints or applications that share common configuration requirements, such as security, QoS, services, etc. Endpoints can be virtual/logical or physical devices, such as VMs, containers, hosts, or physical servers that are connected to Network Environment 100. Endpoints can have one or more attributes such as a VM name, guest OS name, a security tag, application profile, etc. Application configurations can be applied between EPGs, instead of endpoints directly, in the form of contracts. Leafs 104 can classify incoming traffic into different EPGs. The classification can be based on, for example, a network segment identifier such as a VLAN ID, VXLAN Network Identifier (VNID), NVGRE Virtual Subnet Identifier (VSID), MAC address, IP address, etc.

In some cases, classification in the ACI infrastructure can be implemented by Application Virtual Switches (AVS), which can run on a host, such as a server or switch. For example, an AVS can classify traffic based on specified attributes, and tag packets of different attribute EPGs with different identifiers, such as network segment identifiers (e.g., VLAN ID). Finally, Leafs 104 can tie packets with their attribute EPGs based on their identifiers and enforce policies, which can be implemented and/or managed by one or more Controllers 116. Leaf 104 can classify to which EPG the traffic from a host belongs and enforce policies accordingly.

Another example SDN solution is based on VMWARE NSX. With VMWARE NSX, hosts can run a distributed firewall (DFW) which can classify and process traffic. Consider a case where three types of VMs, namely, application, database and web VMs, are put into a single layer-2 network segment. Traffic protection can be provided within the network segment based on the VM type. For example, HTTP traffic can be allowed among web VMs, and disallowed between a web VM and an application or database VM. To classify traffic and implement policies, VMWARE NSX can implement security groups, which can be used to group the specific VMs (e.g., web VMs, application VMs, database VMs). DFW rules can be configured to implement policies for the specific security groups. To illustrate, in the context of the previous example, DFW rules can be configured to block HTTP traffic between web, application, and database security groups.

Returning now to FIG. 1A, Network Environment 100 can deploy different hosts via Leafs 104, Servers 106, Hypervisors 108, VMs 110, Applications 112, and Controllers 116, such as VMWARE ESXi hosts, WINDOWS HYPER-V hosts, bare metal physical hosts, etc. Network Environment 100 may interoperate with a variety of Hypervisors 108, Servers 106 (e.g., physical and/or virtual servers), SDN orchestration platforms, etc. Network Environment 100 may implement a declarative model to allow its integration with application design and holistic network policy.

Controllers 116 can provide centralized access to fabric information, application configuration, resource configuration, application-level configuration modeling for a software-defined network (SDN) infrastructure, integration with management systems or servers, etc. Controllers 116 can form a control plane that interfaces with an application plane via northbound APIs and a data plane via southbound APIs.

As previously noted, Controllers 116 can define and manage application-level model(s) for configurations in Network Environment 100. In some cases, application or device configurations can also be managed and/or defined by other components in the network. For example, a hypervisor or virtual appliance, such as a VM or container, can run a server or management tool to manage software and services in Network Environment 100, including configurations and settings for virtual appliances.

As illustrated above, Network Environment 100 can include one or more different types of SDN solutions, hosts, etc. For the sake of clarity and explanation purposes, various examples in the disclosure will be described with reference to an ACI framework, and Controllers 116 may be interchangeably referenced as controllers, APICs, or APIC controllers. However, it should be noted that the technologies and concepts herein are not limited to ACI solutions and may be implemented in other architectures and scenarios, including other SDN solutions as well as other types of networks which may not deploy an SDN solution.

Further, as referenced herein, the term “hosts” can refer to Servers 106 (e.g., physical or logical), Hypervisors 108, VMs 110, containers (e.g., Applications 112), etc., and can run or include any type of server or application solution. Non-limiting examples of “hosts” can include virtual switches or routers, such as distributed virtual switches (DVS), application virtual switches (AVS), vector packet processing (VPP) switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-V hosts; VMs; DOCKER Containers; etc.

FIG. 1B illustrates another example of Network Environment 100. In this example, Network Environment 100 includes Endpoints 122 connected to Leafs 104 in Fabric 120. Endpoints 122 can be physical and/or logical or virtual entities, such as servers, clients, VMs, hypervisors, software containers, applications, resources, network devices, workloads, etc. For example, an Endpoint 122 can be an object that represents a physical device (e.g., server, client, switch, etc.), an application (e.g., web application, database application, etc.), a logical or virtual resource (e.g., a virtual switch, a virtual service appliance, a virtualized network function (VNF), a VM, a service chain, etc.), a container running a software resource (e.g., an application, an appliance, a VNF, a service chain, etc.), storage, a workload or workload engine, etc. Endpoints 122 can have an address (e.g., an identity), a location (e.g., host, network segment, virtual routing and forwarding (VRF) instance, domain, etc.), one or more attributes (e.g., name, type, version, patch level, OS name, OS type, etc.), a tag (e.g., security tag), a profile, etc.

Endpoints 122 can be associated with respective Logical Groups 118. Logical Groups 118 can be logical entities containing endpoints (physical and/or logical or virtual) grouped together according to one or more attributes, such as endpoint type (e.g., VM type, workload type, application type, etc.), one or more requirements (e.g., policy requirements, security requirements, QoS requirements, customer requirements, resource requirements, etc.), a resource name (e.g., VM name, application name, etc.), a profile, platform or operating system (OS) characteristics (e.g., OS type or name including guest and/or host OS, etc.), an associated network or tenant, one or more policies, a tag, etc. For example, a logical group can be an object representing a collection of endpoints grouped together. To illustrate, Logical Group 1 can contain client endpoints, Logical Group 2 can contain web server endpoints, Logical Group 3 can contain application server endpoints, Logical Group N can contain database server endpoints, etc. In some examples, Logical Groups 118 are EPGs in an ACI environment and/or other logical groups (e.g., SGs) in another SDN environment.

Traffic to and/or from Endpoints 122 can be classified, processed, managed, etc., based Logical Groups 118. For example, Logical Groups 118 can be used to classify traffic to or from Endpoints 122, apply policies to traffic to or from Endpoints 122, define relationships between Endpoints 122, define roles of Endpoints 122 (e.g., whether an endpoint consumes or provides a service, etc.), apply rules to traffic to or from Endpoints 122, apply filters or access control lists (ACLs) to traffic to or from Endpoints 122, define communication paths for traffic to or from Endpoints 122, enforce requirements associated with Endpoints 122, implement security and other configurations associated with Endpoints 122, etc.

In an ACI environment, Logical Groups 118 can be EPGs used to define contracts in the ACI. Contracts can include rules specifying what and how communications between EPGs take place. For example, a contract can define what provides a service, what consumes a service, and what policy objects are related to that consumption relationship. A contract can include a policy that defines the communication path and all related elements of a communication or relationship between endpoints or EPGs. For example, a Web EPG can provide a service that a Client EPG consumes, and that consumption can be subject to a filter (ACL) and a service graph that includes one or more services, such as firewall inspection services and server load balancing.

FIG. 2A illustrates a diagram of an example Management Information Model 200 for an SDN network, such as Network Environment 100. The following discussion of Management Information Model 200 references various terms which shall also be used throughout the disclosure. Accordingly, for clarity, the disclosure shall first provide below a list of terminology, which will be followed by a more detailed discussion of Management Information Model 200.

As used herein, an “Alias” can refer to a changeable name for a given object. Thus, even if the name of an object, once created, cannot be changed, the Alias can be a field that can be changed.

As used herein, the term “Aliasing” can refer to a rule (e.g., contracts, policies, configurations, etc.) that overlaps one or more other rules. For example, Contract 1 defined in a logical model of a network can be said to be aliasing Contract 2 defined in the logical model of the network if Contract 1 overlaps Contract 1. In this example, by aliasing Contract 2, Contract 1 may render Contract 2 redundant or inoperable. For example, if Contract 1 has a higher priority than Contract 2, such aliasing can render Contract 2 redundant based on Contract 1's overlapping and higher priority characteristics.

As used herein, the term “APIC” can refer to one or more controllers (e.g., Controllers 116) in an ACI framework. The APIC can provide a unified point of automation and management, policy programming, application deployment, health monitoring for an ACI multitenant fabric. The APIC can be implemented as a single controller, a distributed controller, or a replicated, synchronized, and/or clustered controller.

As used herein, the term “BDD” can refer to a binary decision tree. A binary decision tree can be a data structure representing functions, such as Boolean functions.

As used herein, the term “BD” can refer to a bridge domain. A bridge domain can be a set of logical ports that share the same flooding or broadcast characteristics. Like a virtual LAN (VLAN), bridge domains can span multiple devices. A bridge domain can be a L2 (Layer 2) construct.

As used herein, a “Consumer” can refer to an endpoint, resource, and/or EPG that consumes a service.

As used herein, a “Context” can refer to an L3 (Layer 3) address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Non-limiting examples of a context or L3 address domain can include a Virtual Routing and Forwarding (VRF) instance, a private network, and so forth.

As used herein, the term “Contract” can refer to rules or configurations that specify what and how communications in a network are conducted (e.g., allowed, denied, filtered, processed, etc.). In an ACI network, contracts can specify how communications between endpoints and/or EPGs take place. In some examples, a contract can provide rules and configurations akin to an Access Control List (ACL).

As used herein, the term “Distinguished Name” (DN) can refer to a unique name that describes an object, such as an MO, and locates its place in Management Information Model 200. In some cases, the DN can be (or equate to) a Fully Qualified Domain Name (FQDN).

As used herein, the term “Endpoint Group” (EPG) can refer to a logical entity or object associated with a collection or group of endoints as previously described with reference to FIG. 1B.

As used herein, the term “Filter” can refer to a parameter or configuration for allowing communications. For example, in a whitelist model where all communications are blocked by default, a communication must be given explicit permission to prevent such communication from being blocked. A filter can define permission(s) for one or more communications or packets. A filter can thus function similar to an ACL or Firewall rule. In some examples, a filter can be implemented in a packet (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer 4) ports, and so on, which is used to allow inbound or outbound communications between endpoints or EPGs, for example.

As used herein, the term “L2 Out” can refer to a bridged connection. A bridged connection can connect two or more segments of the same network so that they can communicate. In an ACI framework, an L2 out can be a bridged (Layer 2) connection between an ACI fabric (e.g., Fabric 120) and an outside Layer 2 network, such as a switch.

As used herein, the term “L3 Out” can refer to a routed connection. A routed Layer 3 connection uses a set of protocols that determine the path that data follows in order to travel across networks from its source to its destination. Routed connections can perform forwarding (e.g., IP forwarding) according to a protocol selected, such as BGP (border gateway protocol), OSPF (Open Shortest Path First), EIGRP (Enhanced Interior Gateway Routing Protocol), etc.

As used herein, the term “Managed Object” (MO) can refer to an abstract representation of objects that are managed in a network (e.g., Network Environment 100). The objects can be concrete objects (e.g., a switch, server, adapter, etc.), or logical objects (e.g., an application profile, an EPG, a fault, etc.). The MOs can be network resources or elements that are managed in the network. For example, in an ACI environment, an MO can include an abstraction of an ACI fabric (e.g., Fabric 120) resource.

As used herein, the term “Management Information Tree” (MIT) can refer to a hierarchical management information tree containing the MOs of a system. For example, in ACI, the MIT contains the MOs of the ACI fabric (e.g., Fabric 120). The MIT can also be referred to as a Management Information Model (MIM), such as Management Information Model 200.

As used herein, the term “Policy” can refer to one or more specifications for controlling some aspect of system or network behavior. For example, a policy can include a named entity that contains specifications for controlling some aspect of system behavior. To illustrate, a Layer 3 Outside Network Policy can contain the BGP protocol to enable BGP routing functions when connecting Fabric 120 to an outside Layer 3 network.

As used herein, the term “Profile” can refer to the configuration details associated with a policy. For example, a profile can include a named entity that contains the configuration details for implementing one or more instances of a policy. To illustrate, a switch node profile for a routing policy can contain the switch-specific configuration details to implement the BGP routing protocol.

As used herein, the term “Provider” refers to an object or entity providing a service. For example, a provider can be an EPG that provides a service.

As used herein, the term “Subject” refers to one or more parameters in a contract for defining communications. For example, in ACI, subjects in a contract can specify what information can be communicated and how. Subjects can function similar to ACLs.

As used herein, the term “Tenant” refers to a unit of isolation in a network. For example, a tenant can be a secure and exclusive virtual computing environment. In ACI, a tenant can be a unit of isolation from a policy perspective, but does not necessarily represent a private network. Indeed, ACI tenants can contain multiple private networks (e.g., VRFs). Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a grouping of policies.

As used herein, the term “VRF” refers to a virtual routing and forwarding instance. The VRF can define a Layer 3 address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Also known as a context or private network.

Having described various terms used herein, the disclosure now returns to a discussion of Management Information Model (MIM) 200 in FIG. 2A. As previously noted, MIM 200 can be a hierarchical management information tree or MIT. Moreover, MIM 200 can be managed and processed by Controllers 116, such as APICs in an ACI. Controllers 116 can enable the control of managed resources by presenting their manageable characteristics as object properties that can be inherited according to the location of the object within the hierarchical structure of the model.

The hierarchical structure of MIM 200 starts with Policy Universe 202 at the top (Root) and contains parent and child nodes 116, 204, 206, 208, 210, 212. Nodes 116, 202, 204, 206, 208, 210, 212 in the tree represent the managed objects (MOs) or groups of objects. Each object in the fabric (e.g., Fabric 120) has a unique distinguished name (DN) that describes the object and locates its place in the tree. The Nodes 116, 202, 204, 206, 208, 210, 212 can include the various MOs, as described below, which contain policies that govern the operation of the system.

Controllers 116

Controllers 116 (e.g., APIC controllers) can provide management, policy programming, application deployment, and health monitoring for Fabric 120.

Node 204

Node 204 includes a tenant container for policies that enable an administrator to exercise domain-based access control. Non-limiting examples of tenants can include:

User tenants defined by the administrator according to the needs of users. They contain policies that govern the operation of resources such as applications, databases, web servers, network-attached storage, virtual machines, and so on.

The common tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of resources accessible to all tenants, such as firewalls, load balancers, Layer 4 to Layer 7 services, intrusion detection appliances, and so on.

The infrastructure tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of infrastructure resources such as the fabric overlay (e.g., VXLAN). It also enables a fabric provider to selectively deploy resources to one or more user tenants. Infrastructure tenant polices can be configurable by the administrator.

The management tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of fabric management functions used for in-band and out-of-band configuration of fabric nodes. The management tenant contains a private out-of-bound address space for the Controller/Fabric internal communications that is outside the fabric data path that provides access through the management port of the switches. The management tenant enables discovery and automation of communications with virtual machine controllers.

Node 206

Node 206 can contain access policies that govern the operation of switch access ports that provide connectivity to resources such as storage, compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtual machine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenant requires interface configurations other than those provided in the default link, Cisco Discovery Protocol (CDP), Link Layer Discovery Protocol (LLDP), Link Aggregation Control Protocol (LACP), or Spanning Tree Protocol (STP), an administrator can configure access policies to enable such configurations on the access ports of Leafs 104.

Node 206 can contain fabric policies that govern the operation of the switch fabric ports, including such functions as Network Time Protocol (NTP) server synchronization, Intermediate System-to-Intermediate System Protocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, Domain Name System (DNS) and so on. The fabric MO contains objects such as power supplies, fans, chassis, and so on.

Node 208

Node 208 can contain VM domains that group VM controllers with similar networking policy requirements. VM controllers can share virtual space (e.g., VLAN or VXLAN space) and application EPGs. Controllers 116 communicate with the VM controller to publish network configurations such as port groups that are then applied to the virtual workloads.

Node 210

Node 210 can contain Layer 4 to Layer 7 service integration life cycle automation framework that enables the system to dynamically respond when a service comes online or goes offline. Policies can provide service device package and inventory management functions.

Node 212

Node 212 can contain access, authentication, and accounting (AAA) policies that govern user privileges, roles, and security domains of Fabric 120.

The hierarchical policy model can fit well with an API, such as a REST API interface. When invoked, the API can read from or write to objects in the MIT. URLs can map directly into distinguished names that identify objects in the MIT. Data in the MIT can be described as a self-contained structured tree text document encoded in XML or JSON, for example.

FIG. 2B illustrates an example object model 220 for a tenant portion of MIM 200. As previously noted, a tenant is a logical container for application policies that enable an administrator to exercise domain-based access control. A tenant thus represents a unit of isolation from a policy perspective, but it does not necessarily represent a private network. Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a convenient grouping of policies. Moreover, tenants can be isolated from one another or can share resources.

Tenant portion 204A of MIM 200 can include various entities, and the entities in Tenant Portion 204A can inherit policies from parent entities. Non-limiting examples of entities in Tenant Portion 204A can include Filters 240, Contracts 236, Outside Networks 222, Bridge Domains 230, VRF Instances 234, and Application Profiles 224.

Bridge Domains 230 can include Subnets 232. Contracts 236 can include Subjects 238. Application Profiles 224 can contain one or more EPGs 226. Some applications can contain multiple components. For example, an e-commerce application could require a web server, a database server, data located in a storage area network, and access to outside resources that enable financial transactions. Application Profile 224 contains as many (or as few) EPGs as necessary that are logically related to providing the capabilities of an application.

EPG 226 can be organized in various ways, such as based on the application they provide, the function they provide (such as infrastructure), where they are in the structure of the data center (such as DMZ), or whatever organizing principle that a fabric or tenant administrator chooses to use.

EPGs in the fabric can contain various types of EPGs, such as application EPGs, Layer 2 external outside network instance EPGs, Layer 3 external outside network instance EPGs, management EPGs for out-of-band or in-band access, etc. EPGs 226 can also contain Attributes 228, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs 122) that have common characteristics or attributes, such as common policy requirements (e.g., security, virtual machine mobility (VMM), QoS, or Layer 4 to Layer 7 services). Rather than configure and manage endpoints individually, they can be placed in an EPG and managed as a group.

Policies apply to EPGs, including the endpoints they contain. An EPG can be statically configured by an administrator in Controllers 116, or dynamically configured by an automated system such as VCENTER or OPENSTACK.

To activate tenant policies in Tenant Portion 204A, fabric access policies should be configured and associated with tenant policies. Access policies enable an administrator to configure other network configurations, such as port channels and virtual port channels, protocols such as LLDP, CDP, or LACP, and features such as monitoring or diagnostics.

FIG. 2C illustrates an example Association 260 of tenant entities and access entities in MIM 200. Policy Universe 202 contains Tenant Portion 204A and Access Portion 206A. Thus, Tenant Portion 204A and Access Portion 206A are associated through Policy Universe 202.

Access Portion 206A can contain fabric and infrastructure access policies. Typically, in a policy model, EPGs are coupled with VLANs. For traffic to flow, an EPG is deployed on a leaf port with a VLAN in a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example.

Access Portion 206A thus contains Domain Profile 236 which can define a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, to be associated to the EPGs. Domain Profile 236 contains VLAN Instance Profile 238 (e.g., VLAN pool) and Attacheable Access Entity Profile (AEP) 240, which are associated directly with application EPGs. The AEP 240 deploys the associated application EPGs to the ports to which it is attached, and automates the task of assigning VLANs. While a large data center can have thousands of active VMs provisioned on hundreds of VLANs, Fabric 120 can automatically assign VLAN IDs from VLAN pools. This saves time compared with trunking down VLANs in a traditional data center.

FIG. 2D illustrates a schematic diagram of example models for a network, such as Network Environment 100. The models can be generated based on specific configurations and/or network state parameters associated with various objects, policies, properties, and elements defined in MIM 200. The models can be implemented for network analysis and assurance, and may provide a depiction of the network at various stages of implementation and levels of the network.

As illustrated, the models can include L_Model 270A (Logical Model), LR_Model 270B (Logical Rendered Model or Logical Runtime Model), Li_Model 272 (Logical Model for i), Ci_Model 274 (Concrete model for i), and/or Hi_Model 276 (Hardware model or TCAM Model for i).

L_Model 270A is the logical representation of various elements in MIM 200 as configured in a network (e.g., Network Environment 100), such as objects, object properties, object relationships, and other elements in MIM 200 as configured in a network. L_Model 270A can be generated by Controllers 116 based on configurations entered in Controllers 116 for the network, and thus represents the logical configuration of the network at Controllers 116. This is the declaration of the “end-state” expression that is desired when the elements of the network entities (e.g., applications, tenants, etc.) are connected and Fabric 120 is provisioned by Controllers 116. Because L_Model 270A represents the configurations entered in Controllers 116, including the objects and relationships in MIM 200, it can also reflect the “intent” of the administrator: how the administrator wants the network and network elements to behave.

L_Model 270A can be a fabric or network-wide logical model. For example, L_Model 270A can account configurations and objects from each of Controllers 116. As previously explained, Network Environment 100 can include multiple Controllers 116. In some cases, two or more Controllers 116 may include different configurations or logical models for the network. In such cases, L_Model 270A can obtain any of the configurations or logical models from Controllers 116 and generate a fabric or network wide logical model based on the configurations and logical models from all Controllers 116. L_Model 270A can thus incorporate configurations or logical models between Controllers 116 to provide a comprehensive logical model. L_Model 270A can also address or account for any dependencies, redundancies, conflicts, etc., that may result from the configurations or logical models at the different Controllers 116.

LR_Model 270B is the abstract model expression that Controllers 116 (e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B can provide the configuration components that would be delivered to the physical infrastructure (e.g., Fabric 120) to execute one or more policies. For example, LR_Model 270B can be delivered to Leafs 104 in Fabric 120 to configure Leafs 104 for communication with attached Endpoints 122. LR_Model 270B can also incorporate state information to capture a runtime state of the network (e.g., Fabric 120).

In some cases, LR_Model 270B can provide a representation of L_Model 270A that is normalized according to a specific format or expression that can be propagated to, and/or understood by, the physical infrastructure of Fabric 120 (e.g., Leafs 104, Spines 102, etc.). For example, LR_Model 270B can associate the elements in L_Model 270A with specific identifiers or tags that can be interpreted and/or compiled by the switches in Fabric 120, such as hardware plane identifiers used as classifiers.

Li_Model 272 is a switch-level or switch-specific model obtained from L_Model 270A and/or LR_Model 270B. Li_Model 272 can project L_Model 270A and/or LR_Model 270B on a specific switch or device i, and thus can convey how L_Model 270A and/or LR_Model 270B should appear or be implemented at the specific switch or device i.

For example, Li_Model 272 can project L_Model 270A and/or LR_Model 270B pertaining to a specific switch i to capture a switch-level representation of L_Model 270A and/or LR_Model 270B at switch i. To illustrate, Li_Model 272 L₁ can represent L_Model 270A and/or LR_Model 270B projected to, or implemented at, Leaf 1 (104). Thus, Li_Model 272 can be generated from L_Model 270A and/or LR_Model 270B for individual devices (e.g., Leafs 104, Spines 102, etc.) on Fabric 120.

In some cases, Li_Model 272 can be represented using JSON (JavaScript Object Notation). For example, Li_Model 272 can include JSON objects, such as Rules, Filters, Entries, and Scopes.

Ci_Model 274 is the actual in-state configuration at the individual fabric member i (e.g., switch i). In other words, Ci_Model 274 is a switch-level or switch-specific model that is based on Li_Model 272. For example, Controllers 116 can deliver Li_Model 272 to Leaf 1 (104). Leaf 1 (104) can take Li_Model 272, which can be specific to Leaf 1 (104), and render the policies in Li_Model 272 into a concrete model, Ci_Model 274, that runs on Leaf 1 (104). Leaf 1 (104) can render Li_Model 272 via the OS on Leaf 1 (104), for example. Thus, Ci_Model 274 can be analogous to compiled software, as it is the form of Li_Model 272 that the switch OS at Leaf 1 (104) can execute.

In some cases, Li_Model 272 and Ci_Model 274 can have a same or similar format. For example, Li_Model 272 and Ci_Model 274 can be based on JSON objects. Having the same or similar format can facilitate objects in Li_Model 272 and Ci_Model 274 to be compared for equivalence or congruence. Such equivalence or congruence checks can be used for network analysis and assurance, as further described herein.

Hi_Model 276 is also a switch-level or switch-specific model for switch i, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actual configuration (e.g., rules) stored or rendered on the hardware or memory (e.g., TCAM memory) at the individual fabric member i (e.g., switch i). For example, Hi_Model 276 can represent the configurations (e.g., rules) which Leaf 1 (104) stores or renders on the hardware (e.g., TCAM memory) of Leaf 1 (104) based on Ci_Model 274 at Leaf 1 (104). The switch OS at Leaf 1 (104) can render or execute Ci_Model 274, and Leaf 1 (104) can store or render the configurations from Ci_Model 274 in storage, such as the memory or TCAM at Leaf 1 (104). The configurations from Hi_Model 276 stored or rendered by Leaf 1 (104) represent the configurations that will be implemented by Leaf 1 (104) when processing traffic.

While Models 272, 274, 276 are shown as device-specific or device-level models, similar models can be generated or aggregated for a collection of fabric members (e.g., Leafs 104 and/or Spines 102) in Fabric 120. When combined, device-specific models, such as Model 272, Model 274, and/or Model 276, can provide a representation of Fabric 120 that extends beyond a particular device. For example, in some cases, Li_Model 272, Ci_Model 274, and/or Hi_Model 276 associated with some or all individual fabric members (e.g., Leafs 104 and Spines 102) can be combined or aggregated to generate one or more aggregated models based on the individual fabric members.

As referenced herein, the terms H Model, T Model, and TCAM Model can be used interchangeably to refer to a hardware model, such as Hi_Model 276. For example, Ti Model, Hi_Model and TCAMi Model may be used interchangeably to refer to Hi_Model 276.

Models 270A, 270B, 272, 274, 276 can provide representations of various aspects of the network or various configuration stages for MIM 200. For example, one or more of Models 270A, 270B, 272, 274, 276 can be used to generate Underlay Model 278 representing one or more aspects of Fabric 120 (e.g., underlay topology, routing, etc.), Overlay Model 280 representing one or more aspects of the overlay or logical segment(s) of Network Environment 100 (e.g., COOP, MPBGP, tenants, VRFs, VLANs, VXLANs, virtual applications, VMs, hypervisors, virtual switching, etc.), Tenant Model 282 representing one or more aspects of Tenant portion 204A in MIM 200 (e.g., security, forwarding, service chaining, QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), Resources Model 284 representing one or more resources in Network Environment 100 (e.g., storage, computing, VMs, port channels, physical elements, etc.), etc.

In general, L_Model 270A can be the high-level expression of what exists in the LR_Model 270B, which should be present on the concrete devices as Ci_Model 274 and Hi_Model 276 expression. If there is any gap between the models, there may be inconsistent configurations or problems.

FIG. 3A illustrates a diagram of an example Assurance Appliance System 300 for network assurance. In this example, Assurance Appliance System 300 can include k VMs 110 operating in cluster mode. VMs are used in this example for explanation purposes. However, it should be understood that other configurations are also contemplated herein, such as use of containers, bare metal devices, Endpoints 122, or any other physical or logical systems. Moreover, while FIG. 3A illustrates a cluster mode configuration, other configurations are also contemplated herein, such as a single mode configuration (e.g., single VM, container, or server) or a service chain for example.

Assurance Appliance System 300 can run on one or more Servers 106, VMs 110, Hypervisors 108, EPs 122, Leafs 104, Controllers 116, or any other system or resource. For example, Assurance Appliance System 300 can be a logical service or application running on one or more VMs 110 in Network Environment 100.

The Assurance Appliance System 300 can include Data Framework 308, which can be based on, for example, APACHE APEX and HADOOP. In some cases, assurance checks can be written as individual operators that reside in Data Framework 308. This enables a natively horizontal scale-out architecture that can scale to arbitrary number of switches in Fabric 120 (e.g., ACI fabric).

Assurance Appliance System 300 can poll Fabric 120 at a configurable periodicity (e.g., an epoch). The analysis workflow can be setup as a DAG (Directed Acyclic Graph) of Operators 310, where data flows from one operator to another and eventually results are generated and persisted to Database 302 for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Spring framework) 304 and Web Server 306. A graphical user interface (GUI) interacts via the APIs exposed to the customer. These APIs can also be used by the customer to collect data from Assurance Appliance System 300 for further integration into other tools.

Operators 310 in Data Framework 308 (e.g., APEX/Hadoop) can together support assurance operations. Below are non-limiting examples of assurance operations that can be performed by Assurance Appliance System 300 via Operators 310.

Security Policy Adherence

Assurance Appliance System 300 can check to make sure the configurations or specification from L_Model 270A, which may reflect the user's intent for the network, including for example the security policies and customer-configured contracts, are correctly implemented and/or rendered in Li_Model 272, Ci_Model 274, and Hi_Model 276, and thus properly implemented and rendered by the fabric members (e.g., Leafs 104), and report any errors, contract violations, or irregularities found.

Static Policy Analysis

Assurance Appliance System 300 can check for issues in the specification of the user's intent or intents (e.g., identify contradictory or conflicting policies in L_Model 270A). Assurance Appliance System 300 can identify lint events based on the intent specification of a network. The lint and policy analysis can include semantic and/or syntactic checks of the intent specification(s) of a network.

TCAM Utilization

TCAM is a scarce resource in the fabric (e.g., Fabric 120). However, Assurance Appliance System 300 can analyze the TCAM utilization by the network data (e.g., Longest Prefix Match (LPM) tables, routing tables, VLAN tables, BGP updates, etc.), Contracts, Logical Groups 118 (e.g., EPGs), Tenants, Spines 102, Leafs 104, and other dimensions in Network Environment 100 and/or objects in MIM 200, to provide a network operator or user visibility into the utilization of this scarce resource. This can greatly help for planning and other optimization purposes.

Endpoint Checks

Assurance Appliance System 300 can validate that the fabric (e.g. fabric 120) has no inconsistencies in the Endpoint information registered (e.g., two leafs announcing the same endpoint, duplicate subnets, etc.), among other such checks.

Tenant Routing Checks

Assurance Appliance System 300 can validate that BDs, VRFs, subnets (both internal and external), VLANs, contracts, filters, applications, EPGs, etc., are correctly programmed.

Infrastructure Routing

Assurance Appliance System 300 can validate that infrastructure routing (e.g., IS-IS protocol) has no convergence issues leading to black holes, loops, flaps, and other problems.

MP-BGP Route Reflection Checks

The network fabric (e.g., Fabric 120) can interface with other external networks and provide connectivity to them via one or more protocols, such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF), etc. The learned routes are advertised within the network fabric via, for example, MP-BGP. These checks can ensure that a route reflection service via, for example, MP-BGP (e.g., from Border Leaf) does not have health issues.

Logical Lint and Real-time Change Analysis

Assurance Appliance System 300 can validate rules in the specification of the network (e.g., L_Model 270A) are complete and do not have inconsistencies or other problems. MOs in the MIM 200 can be checked by Assurance Appliance System 300 through syntactic and semantic checks performed on L_Model 270A and/or the associated configurations of the MOs in MIM 200. Assurance Appliance System 300 can also verify that unnecessary, stale, unused or redundant configurations, such as contracts, are removed.

FIG. 3B illustrates an architectural diagram of an example system 350 for network assurance, such as Assurance Appliance System 300. System 350 can include Operators 312, 314, 316, 318, 320, 322, 324, and 326. In some cases, Operators 312, 314, 316, 318, 320, 322, 324, and 326 can correspond to Operators 310 previously discussed with respect to FIG. 3A. For example, Operators 312, 314, 316, 318, 320, 322, 324, and 326 can each represent one or more of the Operators 310 in Assurance Appliance System 300.

In this example, Topology Explorer 312 communicates with Controllers 116 (e.g., APIC controllers) in order to discover or otherwise construct a comprehensive topological view of Fabric 120 (e.g., Spines 102, Leafs 104, Controllers 116, Endpoints 122, and any other components as well as their interconnections). While various architectural components are represented in a singular, boxed fashion, it is understood that a given architectural component, such as Topology Explorer 312, can correspond to one or more individual Operators 310 and may include one or more nodes or endpoints, such as one or more servers, VMs, containers, applications, service functions (e.g., functions in a service chain or virtualized network function), etc.

Topology Explorer 312 is configured to discover nodes in Fabric 120, such as Controllers 116, Leafs 104, Spines 102, etc. Topology Explorer 312 can additionally detect a majority election performed amongst Controllers 116, and determine whether a quorum exists amongst Controllers 116. If no quorum or majority exists, Topology Explorer 312 can trigger an event and alert a user that a configuration or other error exists amongst Controllers 116 that is preventing a quorum or majority from being reached. Topology Explorer 312 can detect Leafs 104 and Spines 102 that are part of Fabric 120 and publish their corresponding out-of-band management network addresses (e.g., IP addresses) to downstream services. This can be part of the topological view that is published to the downstream services at the conclusion of Topology Explorer's 312 discovery epoch (e.g., 5 minutes, or some other specified interval).

In some examples, Topology Explorer 312 can receive as input a list of Controllers 116 (e.g., APIC controllers) that are associated with the network/fabric (e.g., Fabric 120). Topology Explorer 312 can also receive corresponding credentials to login to each controller. Topology Explorer 312 can retrieve information from each controller using, for example, REST calls. Topology Explorer 312 can obtain from each controller a list of nodes (e.g., Leafs 104 and Spines 102), and their associated properties, that the controller is aware of. Topology Explorer 312 can obtain node information from Controllers 116 including, without limitation, an IP address, a node identifier, a node name, a node domain, a node URI, a node_dm, a node role, a node version, etc.

Topology Explorer 312 can also determine if Controllers 116 are in quorum, or are sufficiently communicatively coupled amongst themselves. For example, if there are n controllers, a quorum condition might be met when (n/2+1) controllers are aware of each other and/or are communicatively coupled. Topology Explorer 312 can make the determination of a quorum (or identify any failed nodes or controllers) by parsing the data returned from the controllers, and identifying communicative couplings between their constituent nodes. Topology Explorer 312 can identify the type of each node in the network, e.g. spine, leaf, APIC, etc., and include this information in the topology information generated (e.g., topology map or model).

If no quorum is present, Topology Explorer 312 can trigger an event and alert a user that reconfiguration or suitable attention is required. If a quorum is present, Topology Explorer 312 can compile the network topology information into a JSON object and pass it downstream to other operators or services, such as Unified Collector 314.

Unified Collector 314 can receive the topological view or model from Topology Explorer 312 and use the topology information to collect information for network assurance from Fabric 120. Unified Collector 314 can poll nodes (e.g., Controllers 116, Leafs 104, Spines 102, etc.) in Fabric 120 to collect information from the nodes.

Unified Collector 314 can include one or more collectors (e.g., collector devices, operators, applications, VMs, etc.) configured to collect information from Topology Explorer 312 and/or nodes in Fabric 120. For example, Unified Collector 314 can include a cluster of collectors, and each of the collectors can be assigned to a subset of nodes within the topological model and/or Fabric 120 in order to collect information from their assigned subset of nodes. For performance, Unified Collector 314 can run in a parallel, multi-threaded fashion.

Unified Collector 314 can perform load balancing across individual collectors in order to streamline the efficiency of the overall collection process. Load balancing can be optimized by managing the distribution of subsets of nodes to collectors, for example by randomly hashing nodes to collectors.

In some cases, Assurance Appliance System 300 can run multiple instances of Unified Collector 314. This can also allow Assurance Appliance System 300 to distribute the task of collecting data for each node in the topology (e.g., Fabric 120 including Spines 102, Leafs 104, Controllers 116, etc.) via sharding and/or load balancing, and map collection tasks and/or nodes to a particular instance of Unified Collector 314 with data collection across nodes being performed in parallel by various instances of Unified Collector 314. Within a given node, commands and data collection can be executed serially. Assurance Appliance System 300 can control the number of threads used by each instance of Unified Collector 314 to poll data from Fabric 120.

Unified Collector 314 can collect models (e.g., L_Model 270A and/or LR_Model 270B) from Controllers 116, switch software configurations and models (e.g., Ci_Model 274) from nodes (e.g., Leafs 104 and/or Spines 102) in Fabric 120, hardware configurations and models (e.g., Hi_Model 276) from nodes (e.g., Leafs 104 and/or Spines 102) in Fabric 120, etc. Unified Collector 314 can collect Ci_Model 274 and Hi_Model 276 from individual nodes or fabric members, such as Leafs 104 and Spines 102, and L_Model 270A and/or LR_Model 270B from one or more controllers (e.g., Controllers 116) in Network Environment 100.

Unified Collector 314 can poll the devices that Topology Explorer 312 discovers in order to collect data from Fabric 120 (e.g., from the constituent members of the fabric). Unified Collector 314 can collect the data using interfaces exposed by Controllers 116 and/or switch software (e.g., switch OS), including, for example, a Representation State Transfer (REST) Interface and a Secure Shell (SSH) Interface.

In some cases, Unified Collector 314 collects L_Model 270A, LR_Model 270B, and/or Ci_Model 274 via a REST API, and the hardware information (e.g., configurations, tables, fabric card information, rules, routes, etc.) via SSH using utilities provided by the switch software, such as virtual shell (VSH or VSHELL) for accessing the switch command-line interface (CLI) or VSH_LC shell for accessing runtime state of the line card.

Unified Collector 314 can poll other information from Controllers 116, including, without limitation: topology information, tenant forwarding/routing information, tenant security policies, contracts, interface policies, physical domain or VMM domain information, OOB (out-of-band) management IP's of nodes in the fabric, etc.

Unified Collector 314 can also poll information from nodes (e.g., Leafs 104 and Spines 102) in Fabric 120, including without limitation: Ci_Models 274 for VLANs, BDs, and security policies; Link Layer Discovery Protocol (LLDP) connectivity information of nodes (e.g., Leafs 104 and/or Spines 102); endpoint information from EPM/COOP; fabric card information from Spines 102; routing information base (RIB) tables from nodes in Fabric 120; forwarding information base (FIB) tables from nodes in Fabric 120; security group hardware tables (e.g., TCAM tables) from nodes in Fabric 120; etc.

In some cases, Unified Collector 314 can obtain runtime state from the network and incorporate runtime state information into L_Model 270A and/or LR_Model 270B. Unified Collector 314 can also obtain multiple logical models from Controllers 116 and generate a comprehensive or network-wide logical model (e.g., L_Model 270A and/or LR_Model 270B) based on the logical models. Unified Collector 314 can compare logical models from Controllers 116, resolve dependencies, remove redundancies, etc., and generate a single L_Model 270A and/or LR_Model 270B for the entire network or fabric.

Unified Collector 314 can collect the entire network state across Controllers 116 and fabric nodes or members (e.g., Leafs 104 and/or Spines 102). For example, Unified Collector 314 can use a REST interface and an SSH interface to collect the network state. This information collected by Unified Collector 314 can include data relating to the link layer, VLANs, BDs, VRFs, security policies, etc. The state information can be represented in LR_Model 270B, as previously mentioned. Unified Collector 314 can then publish the collected information and models to any downstream operators that are interested in or require such information. Unified Collector 314 can publish information as it is received, such that data is streamed to the downstream operators.

Data collected by Unified Collector 314 can be compressed and sent to downstream services. In some examples, Unified Collector 314 can collect data in an online fashion or real-time fashion, and send the data downstream, as it is collected, for further analysis. In some examples, Unified Collector 314 can collect data in an offline fashion, and compile the data for later analysis or transmission.

Assurance Appliance System 300 can contact Controllers 116, Spines 102, Leafs 104, and other nodes to collect various types of data (e.g., via Unified Collector 314). In some scenarios, Assurance Appliance System 300 or Unified Collector 314 may experience a failure (e.g., connectivity problem, hardware or software error, etc.) that prevents it from being able to collect data for a period of time. Such failures can originate from one or more components in Assurance Appliance System 300 (e.g., Unified Collector 314) and/or other components in Network Environment 100 or Fabric 120, such as one or more nodes in Fabric 120 (e.g., Controllers 116, Leafs 104, Spines 102, etc.). Assurance Appliance System 300 and/or Unified Collector 314 can handle such failures seamlessly, and generate events or responses based on such failures.

For example, if Unified Collector 314 is unable to collect information from one or more nodes (e.g., due to a network, hardware or software failure), then Unified Collector 314 can trigger an event or response to address the detected failure. Thus, while data collection can be automated by Unified Collector 314, Unified Collector 314 can generate alerts when data collection is not possible or will result in an incomplete record, in order to avoid or limit scenarios that may result in error due to incomplete data collection and enable quicker resolution. Such failure handling procedures can also provide a safeguard layer to prevent wasted computational effort.

Unified Collector 314 can generate alerts or events, as described above, when a failure scenario occurs or is detected. However, in some cases, alerts or events can be generated by a separate Event Service (Not Shown), by Unified Collector 314, and/or both. For example, in Unified Collector 314 can communicate with an Event Service to inform the Event Service of a collection status, condition, failure, etc., and/or to report to the Event Service what data has been collected. This can allow the Event Service to ascertain when a failure has occurred in order to generate an alert or event. The Event Service can be implemented by one or more operators, applications, servers, appliances, functions (e.g., service function chain), VMs, modules, etc. However, in some examples, the Event Service can be implemented via one or more components in Unified Collector 314. Thus, the Event Service can, in some case, be part of Unified Collector 314.

Switch Logical Policy Generator 316 can receive L_Model 270A and/or LR_Model 270B from Unified Collector 314 and calculate Li_Model 272 for each network device i (e.g., switch i) in Fabric 120. For example, Switch Logical Policy Generator 316 can receive L_Model 270A and/or LR_Model 270B and generate Li_Model 272 by projecting a logical model for each individual node i (e.g., Spines 102 and/or Leafs 104) in Fabric 120. Switch Logical Policy Generator 316 can generate Li_Model 272 for each switch in Fabric 120, thus creating a switch logical model based on L_Model 270A and/or LR_Model 270B for each switch.

Each Li_Model 272 can represent L_Model 270A and/or LR_Model 270B as projected or applied at the respective network device i (e.g., switch i) in Fabric 120. In some cases, Li_Model 272 can be normalized or formatted in a manner that is compatible with the respective network device. For example, Li_Model 272 can be formatted in a manner that can be read or executed by the respective network device. To illustrate, Li_Model 272 can included specific identifiers (e.g., hardware plane identifiers used by Controllers 116 as classifiers, etc.) or tags (e.g., policy group tags) that can be interpreted by the respective network device. In some cases, Li_Model 272 can include JSON objects. For example, Li_Model 272 can include JSON objects to represent rules, filters, entries, scopes, etc.

The format used for Li_Model 272 can be the same as, or consistent with, the format of Ci_Model 274. For example, both Li_Model 272 and Ci_Model 274 may be based on JSON objects. Similar or matching formats can enable Li_Model 272 and Ci_Model 274 to be compared for equivalence or congruence. Such equivalency checks can aid in network analysis and assurance as further explained herein.

Switch Logical Configuration Generator 316 can also perform change analysis and generate lint events or records for problems discovered in L_Model 270A and/or LR_Model 270B. The lint events or records can be used to generate alerts for a user or network operator.

Policy Operator 318 can receive Ci_Model 274 and Hi_Model 276 for each switch from Unified Collector 314, and Li_Model 272 for each switch from Switch Logical Policy Generator 316, and perform assurance checks and analysis (e.g., security adherence checks, TCAM utilization analysis, etc.) based on Ci_Model 274, Hi_Model 276, and Li_Model 272. Policy Operator 318 can perform assurance checks on a switch-by-switch basis by comparing one or more of the models.

Returning to Unified Collector 314, Unified Collector 314 can also send L_Model 270A and/or LR_Model 270B to Routing Policy Parser 320, and Ci_Model 274 and Hi_Model 276 to Routing Parser 326.

Routing Policy Parser 320 can receive L_Model 270A and/or LR_Model 270B and parse the model(s) for information that may be relevant to downstream operators, such as Endpoint Checker 322 and Tenant Routing Checker 324. Similarly, Routing Parser 326 can receive Ci_Model 274 and Hi_Model 276 and parse each model for information for downstream operators, Endpoint Checker 322 and Tenant Routing Checker 324.

After Ci_Model 274, Hi_Model 276, L_Model 270A and/or LR_Model 270B are parsed, Routing Policy Parser 320 and/or Routing Parser 326 can send cleaned-up protocol buffers (Proto Buffs) to the downstream operators, Endpoint Checker 322 and Tenant Routing Checker 324. Endpoint Checker 322 can then generate events related to Endpoint violations, such as duplicate IPs, APIPA, etc., and Tenant Routing Checker 324 can generate events related to the deployment of BDs, VRFs, subnets, routing table prefixes, etc.

Assurance Appliance System 300 Failure Handling and Appliance Telemetry

As previously noted, Assurance Appliance System 300 has various domains, including North Tier and South Tier, which can experience failures. Assurance Appliance System 300 can be configured to detect and respond to various types of failures for each of these domains.

For example, the North Tier can provide a consumption layer for north bound consumption via, for example, an API and UI. The North Tier can include, for example, Web, App, and DB Tiers.

The South Tier can include data collection and data analysis tiers. The South Tier can involve South Infra and South Applications. A failure can be experienced in South Infra or in individual operators or the base OS.

The base OS can vary based on implementation. Failures of concern at the base OS can include, without limitation, resource exhaustion of CPU, memory, disk volume, disk I/O and network I/O, process crash and restarts, OS crashes and reboots, network connectivity failures and storage failures. Below are example tables illustrating example failures and failure responses at various tiers.

TABLE 1 Failures and Failure Handling - North Tier Pri- Heading Details ority Web Tier Partial Appliance can continue to support north P1 Failure API requests with degraded performance. Process Existing user sessions should not be reset. crash Major Event and Alarm can be raised for Process each web tier instance that is in failure state. suppressed The event/alarm can be visible through VM down API (P1). NW Failure The event/alarm can be visible through SSH CLI on monitoring node (P2). Web Tier Full Existing user sessions should not be reset, P1 Failure but may timeout based on specific timeout Process settings. crash Critical Event/Alarm can be raised (this is Process separate from individual event/alarm for suppressed each web tier instance failure). VM down The event/alarm can be visible through API NW Failure (P1) once web tier recovers and via SSH CLI on monitoring node (P2). Web Tier VM Web Tier VMs can be placed on different P1 placement servers when multiple servers are available. App Tier Partial Appliance can continue to support north P1 Failure API requests with degraded performance. Process Existing user sessions should not be reset. crash Major Event and Alarm can be raised for Process each app tier instance that is in failure state. suppressed The event/alarm can be visible through API VM down (P1) and through SSH CLI on monitoring NW Failure node (P2). App Tier Full Existing user sessions should not be reset, P1 Failure but can timeout based on specific timeout Process settings. crash Critical Event/Alarm can be raised (this can Process be separate from individual event/alarm for suppressed each web tier instance failure). VM down The event/alarm can be visible through NW Failure API(P1) when app tier recovers and via SSH CLI on monitoring node (P2). Notification In case of partial or full failure of North App P2 Service tier, it is possible that the notification service may be backlogged. In case of full failure the backlog can be severe. The service can prioritize sending notifications based on severity level of events/alarms instead of time if backlogged. App Tier VM App Tier VMs can be placed on different P1 placement servers when multiple servers are available. DB Tier Single DB Tier can be sized such that, without P1 Failure Server, a DB node failure has no performance Process impacted based on giving sizing of appliance crash when the DB node is recovering supporting Process sharding/replication of new north/south data suppressed as well as sharding/replication of old VM down north/south data (data recovery). NW Failure Major Event/Alarm can be raised available via API (P1) and SSH CLI (P2). DB Tier Multiple Up to the number of failures that the DB P1 Failure without tier can handle without loss of data loss of data consistency, it can continue to support north consistency services and support south services at Process degraded performance. crash Depending on architecture approach, data Process backlog from South tier to North DB can suppressed impact ability of south tier to do analysis and VM down may degrade performance for south tier (this NW Failure may be acceptable). Major Event/Alarm can be raised for each DB failure and can be available via API (P1) and SSH CLI (P2). DB Tier Multiple If numbers of failures starts impacting data P1 Failures with data consistency, then DB tier can stop service consistency loss even if all nodes have not failed. Appliance can also stop South analysis if South Tier depends on DB tier. Major Event/Alarm can be raised for each DB failure and available via API (P1) and SSH CLI (P2). Critical Event/Alarm can be raised indicating DB tier is no longer available and available via API (P1) and SSH CLI (P2). Critical Event/Alarm can be raised indicating Appliance analysis has stopped and can be available via API (P1) and SSH CLI (P2). DB Usage DB resources (read/write access) can be P2 Preference on made available to South Service to finish Degraded analysis in situation of degraded DB Performance performance due to DB tier partial failure. DB Tier VM DB Tier VMs can be placed on different P1 placement servers when multiple servers are available. For improved Disk I/O VM placement guidance can be such that it reduces or avoids contention between VMs to the same disk I/O controller. DB Tier Capacity DB tier can auto purge results based on P1 Exhaustion age (e.g., purge oldest first) if it is near capacity. Major Event/Alarm can be raised for the above. North Tier North tier can continue to serve data P1 Behavior on independent of south tier failures. South Tier Failures Appliance Health Health of North Tier and its services can be P1 Monitoring UI made available on Appliance Health Screen Monitoring UI Screen.

TABLE 2 Failures and Failure Handling - South Tier Pri- Heading Details ority Deterministic In case of full failure of an infra service (e.g., P1 Error State Name Node, Resource Manager, All Data and Recovery Nodes of HDFS), Data Collection and Data from same Analysis can be in deterministic error state. Process crash The above failure can be due to clustering Process majority failure or due to the infra service suppressed itself failing completely. VM down Deterministic error state can be completely NW Failure stopping Data Collection or Data Analysis or both or pausing them so that they can be restarted safely when majority is established. No bad analysis results should be generated due to failure and on recovery from failure. Partial clean results can be made available. Critical event/alarm can be raised if Data Collection or Data Analysis will be stopped or paused due to above. Partial Failure of If Infra services have partial failure but can P1 Infra Services continue safely working then the Data Process Collection and Data Analysis can continue to crash run (may have degraded performance). Process Collection and Analysis can be resilient to suppressed switchover from master to backup for a given VM down infra service (e.g., Node Name switchover or NW Failure resource manager switchover, etc.). No bad analysis results should be generated due to failure and on recovery from failure. Partial clean results should be made available. Major event/alarm should be raised if Data Collection or Data Analysis will be stopped or paused due to above. This requirement covers following based on an example appliance implementation, such as: HDFS Name Node (Active/Backup); HDFS Data Node; Yarn Resource Manager (Active/Backup); Yarn Node Manager; Apex Application Master; Zookeeper nodes; Journal nodes. South Tier South Storage can auto purge results based on P1 Storage Capacity age (purge oldest first) if it is near capacity. Exhaustion Major Event/Alarm can be raised for the above. North DB Tier Since south results may be eventually stored in P1 Storage Capacity North DB, the north DB tier can auto purge Exhaustion results based on age (e.g., purge oldest first) if it is near capacity. Inspector or South tier can provide resiliency for individual P1 Micro service microservice failure. (Operator) This includes out of memory failures due to Failures memory leak, for example. Ground Hog Day If microservice is continually failing (e.g., due P1 to code bug, or memory exhaustion unrelated to memory leak), then results for other microservices should not be impacted, but the one for the failing microservice can be suppressed. System can ensure it is able to move to analysis of next Epoch and not permanently stuck analyzing current Epoch. No bad analysis results should be generated due to failure and on recovery from failure. Partial clean results should be made available. Degraded performance when above happens may be acceptable. Event/Alarm can be raised when the above happens via, e.g., API (P1) and SSH CLI (P2). Long DAG If system is taking long (e.g., 5X rated P1 Analysis Run Analysis interval time based on appliance time for single sizing), then it can stop analysis for the Epoch. Epoch No bad analysis results should be generated Due to out of due to failure and on recovery from failure. capacity Partial clean results should be made available. Due to Major event/alarm can be raised for the above. infra/operator failures

TABLE 3 Appliance Telemetry for Base OS Pri- Heading Details ority Appliance Base OS CPU utilization trend per VM. P1 Health Telemetry Memory utilization trend per VM. Swapping trend per VM. Disk Volume utilization trend per VM. Disk I/O trend per VM. Network I/O trend per VM. Process crash information. Process and kernel core information. Appliance North and Health status trend with no failure, partial P1 South Service Health failure and full failure for each North Tier Telemetry Service (Web, App, DB), South Tier Service (HDFS Name Node, HDFS Data Node, Zookeeper, Yarn Node Manager, Yarn Resource Manager, Application Manager). Any other failure/recovery status needed for serviceability or to analyze and fix CFDs. South Tier Storage (HDFS) capacity trend per VM (if applicable) and as a whole. North Tier Storage (DB) capacity trend per VM (if applicable) and as a whole. South Tier HDFS recovery status. North Tier DB recovery status. Operator health status (e.g., operator failure/crash and reasons). Appliance Troubleshoot a CFD about appliance running P2 Performance slow Telemetry API processing times per API. DAG Collection times per assured entity. DAG analysis times per assured entity. DB/HDFS Performance. Trend on number of APIs on a user basis and total across users. Additional Major: Continuous memory swapping P1 Events/Alarms beyond X minutes. Major: Total disk at X threshold (e.g., 90%). Critical: Total disk at Y threshold (e.g., 95%). VM resources over- Detect and provide this if the hypervisor P2 subscription on provides access to this information to guest server OS. CPU Memory Network Disk Log information for Centralized logging can be available for Serviceability serviceability as needed. Log access can be available via SSH CLI for the operator (P1) and API (P2). Collection and Analysis

Assurance Appliance System 300 can implement rules for collection and analysis, as well as failure handling. Below is an example are rules and conditions for collection and analysis and failure handling, as well as a description of failure handling scenarios. Other configurations are also contemplated herein.

Rule 1: Collect the logical model (e.g., L_Model 270) only from controllers that are in a majority and have a same software version which is a software version supported by Assurance Appliance System 300.

Rule 2: From the collected logical models, create a list of all nodes which are registered (node id is not zero) and in Active or Inactive state. Any nodes in DISABLED, DISCOVERING, UNDISCOVERED or UNKNOWN states are excluded from the list.

Rule 3: Analyze each node in the list of nodes from Rule 2 which have a software that is supported by Assurance Appliance System 300. Nodes with unsupported software are excluded from the analysis.

Rule 4: For each node having a supported software, and a hardware that is at least partially supported by a set of inspectors associated with Assurance Appliance System 300, construct a respective Ci_Model 274 from the logical model and a respective Hi_Model 276 from the respective Ci_Model 274. In other words, for nodes having a supported software and at least partially supported hardware, transform the logical model to a concrete model and the concrete model to a hardware model.

Rule 5: For each node having a supported software but a hardware unsupported by the set of inspectors, construct a respective Ci_Model 274 from the logical model but do not construct a respective Hi_Model 276 from the respective Ci_Model 274. The inspectors may also generate an event or alert for those nodes with an unsupported hardware.

Failure Handling

Assurance Appliance System 300 can also implement rules for failure handling. Failure handling can be based on the status of the various elements in a network, including a controller status, a node status from the perspective of a controller, a node status from the perspective of Assurance Appliance System 300, etc. Below is a description of example status conditions, as well as a set of example rules for failure handling. Other rules and states are also contemplated herein.

Controller Status:

If a controller is not reachable or a reachable controller is not part of a majority, an analysis (e.g., DAG Analysis) can be stopped and an event or alarm generated (e.g., Critical Event/Alarm (Emergency)).

If a controller is reachable but partitioned with no majority, the analysis (e.g., DAG Analysis) can be stopped and an event or alarm generated (e.g., Critical Event/Alarm (Emergency)).

If a controller release is unsupported but a node's software is supported, an analysis (e.g., DAG Analysis) can be stopped and an event or alarm generated (e.g., Critical Event/Alarm (Emergency)).

If a set of controllers is reachable and in majority but some of the controllers have a software version that is not supported and others have a software version that is supported, the controller can operate at the lowest software version of the controllers participating in majority. The system may not run the analysis (e.g., DAG Analysis) and may wait until all controllers have a same software version and that version is supported.

If reachable controllers are in majority and have mixed software versions that are supported by the system, the system may not run the analysis (e.g., DAG Analysis). The system can raise an event or alarm for each case that is not supported, and the operators (e.g., Operators 310) can continue to run at the analysis interval.

Node Status from the Controller's Perspective:

Assurance Appliance System 300 may not collect data from nodes having a status of disabled, undiscovered/discovering, unsupported, or unknown. In these cases, the Assurance Appliance System 300 can generate, per interval (e.g., Epoch), an INFO event indicating the list of registered nodes in the disabled, undiscovered/discovering, unsupported, or unknown state, and which will not be included in the data collection.

Assurance Appliance System 300 may collect data from nodes otherwise having an inactive or active status.

Node Status from the Perspective of the Assurance Appliance:

One or more nodes (e.g., Spines 102 and Leafs 104) may not be reachable by Assurance Appliance System 300. If one or more nodes are not reachable, inspectors can suppress false positives, and an event or alarm can be raised for unreachable nodes indicating that analysis cannot be completed for those nodes because the nodes are not reachable. Infra routing may generate partial information as it is still accurate.

In some cases, one or more nodes may be reachable by Assurance Appliance System 300 but not by a controller that is in majority (e.g., Network partition where certain nodes can only reach a controller which is not part of controller majority but such nodes are reachable by Assurance Appliance System 300). Nodes with this status can be excluded from analysis, but in other cases may be included depending on one or more factors, such as connectivity history. An event or alarm can be generated for nodes in such status to indicate that the nodes are not reachable by a controller in majority but are otherwise reachable by Assurance Appliance System 300 and/or other controller(s).

One or more nodes may have software or hardware that is unsupported by Assurance Appliance System 300. As previously indicated and further noted in table 5 below, rules for collection can be defined for nodes with this status.

One or more nodes may have data that cannot be parsed by one or more inspectors or operators in the Assurance Appliance System 300. In cases where there is a partial failure in parsing from the perspective of one or more inspectors, the inspectors that can parse the data may perform an inspection. In cases where there is a complete failure in parsing from the perspective of one or more inspectors, the inspectors that can raise an event or alarm identifying the parsing failure. In some cases, the inspectors may generate a single micro event or alarm, or a single curated event per fabric.

A single or multiple link failure may exist between nodes (e.g., a Spine and Leaf node). This can be reflected as an IS-IS RIB entry removal.

Table 5 below illustrates example rules on collection and analysis and failure handling events.

TABLE 5 Example Rules for Collection and Analysis Pri- Heading Details ority Rules on Collection Step 1) Collect Logical Model (e.g., Logical P1 and Analysis Model 270) only from a) Controllers 116 that are in majority, b) have the same software version, and c) the software version is supported by the Assurance Appliance. Step 2) From the Logical Models collected, make a list of nodes that are registered (node id is non-zero) and in ACTIVE or INACTIVE state. Nodes in other states, such as DISABLED, DISCOVERING, UNDISCOVERED or UNKNOWN states, can be excluded from the list. Step 3) For each node in the list of nodes having an unsupported software version, do not analyze that node. Step 4) For each node in the list of nodes having a supported software version and a hardware that is at least partially supported, construct Ci_Model 274 from the Logical Model and/or Li_Model 272, and construct Hi_Model 276 from the Ci_Model 274. Step 5) For each node in the list of nodes having a supported software version and an unsupported hardware, construct Ci_Model 274 from the Logical Model and/or Li_Model 272, but do not construct Hi_Model 276 from the Ci_Model 274. Logical Model If the Logical Model cannot be collected, P1 Collection Failure then do not run analysis comparing the Logical Model and the Ci_Model 274. Generate Event/Alarm when Logical Model cannot be collected. Run the analysis (e.g., Ci_Model 274 and Hi_Model 276) by inspectors that support it. Mark analysis results (e.g., Ci_Model 274 and Hi_Model 276 comparison) with information indicating that the Logical Model was not fully collected where such marking makes sense. Logical Model Inspectors that cannot generate some or all Parsing Failure results due to the type of failure can suppress the specific analysis and associated results. Other inspectors should continue to analyze and generate results. Partial Node Generate partial results for all inspectors P1 Collection - except some results of Infra Routing (See Collection failed on Below). certain nodes Infra Routing may skip Routing Loop, Prefix Unreachable and Prefix Isolated. Generate event, such as a single curated event, which indicates only partial results are available. The event can have information on which nodes results could not be generated and which fabric-wide checks could not be performed (e.g., Infra Routing prefix isolation and prefix unreachable checks). Raise a major event for each node that cannot be reached. Partial Node Infra Routing may provide Prefix Unreachable P2 Collection Failure and Prefix Isolated results if it is possible even and Infra Routing if the results are only for a subset of prefixes. Results Parsing Inspectors that cannot generate some or all results due to the type of failure can suppress the specific analysis and associated results. Other inspectors can continue to analyze and generate results. UI indicator for When viewing results of an Epoch where P1 Partial Results results are partial results, a special indicator for this can be provided on the UI at epoch indicator level. When viewings results for an Epoch on a specific inspector page, if the inspector cannot show any results or only has partial results, a special indicator may be needed. When viewings result for an Epoch on a specific inspector page and the customer has made a filter selection and the selected result is partial (e.g., Only Ci_Model 274 to Hi_Model 276 was done but the Logical Model to the Ci_Model 274 could not be done for a specific selected node), then the result can have a special indicator.

Assurance Appliance System 300 and/or its operators (e.g., Unified Collector 314) can address various types of failures and process various alerts or responses. Non-limiting examples of collection and analysis rules, failure scenarios and responses are illustrated in Table 6 below.

TABLE 6 Example Failures and Responses Category Cases Description Processing Controller is Controller is Not Assurance Appliance System Data collection will Reachable reachable 300 raises an event when any be based on whether of Controllers 116 are not a majority quorum is reachable from Assurance reachable. Appliance System 300. Controller Login Assurance Appliance System Data collection will Failed 300 raises an event if it can't be based on whether login to a reachable a majority quorum is Controller. reachable and login is successful. Controller Cluster Controller If Assurance Appliance Data collection will is in Quorum Cluster System 300 detects that a be based on whether Partitioned Controller cluster is a majority quorum is partitioned, it raises a reachable. corresponding event. Reachable If Assurance Appliance Data collection will Controller is not System 300 detects that a be based on whether part of majority Controller cluster is a majority quorum is partitioned, it raises an event reachable. for the Controller that is not part of the majority. No majority If Assurance Appliance No data is collected. Controller cluster System 300 detects that a is reachable Controller cluster is partitioned, it raises an event if none of the partitions has a majority. Controller Unsupported Assurance Appliance System No data is collected. Version Check Controller 300 will raise an alert if a Version Controller is running an unsupported version. Different If different Controllers in No data is collected. Controller cluster are running different Versions software versions, Assurance Appliance System 300 will raise an event for the same. Node Status Node Unknown Assurance Appliance System No data is collected (e.g., Leafs 104 300 raises an event if a node for the unknown and Spines 102) status received from a node. Controllers is: DISABLED UNDISCOVERED DISCOVERING UNKNOWN Node Inactive Assurance Appliance System No data is collected (Controller says it 300 raises an event if it a for the unknown cannot reach the node status of inactive is node. Node) received from a Controller. Node Data Unsupported Assurance Appliance System No data is collected Collection Node Version 300 will raise an alert if a for the given node. node is running an unsupported version. Node is not Assurance Appliance System No data is collected reachable 300 will raise an alert if it is for the given Node. not able to reach the node.

Additional examples of failure events and failure handling operations are provided below. These examples are non-limiting and can be implemented in addition to, or in lieu of, any of the previous examples.

Controller Login Failure

If Assurance Appliance System 300 is unable to login to the Controllers 116, Assurance Appliance System 300 may be unable to proceed with a collection of logical models and/or data from Controllers 116.

In response to such login failure, Assurance Appliance System 300 can check reachability of Controllers 116. For example, Assurance Appliance System 300 attempts to communicate with each controller (e.g., ping) and determine if it is capable of reaching the controller. Assurance Appliance System 300 can also check if a security rule or network parameter configured on the network is preventing Assurance Appliance System 300 from logging in. For example, Assurance Appliance System 300 can check if HTTPS and/or SSH protocols used to login are permitted for communications to/from Assurance Appliance System 300. If Assurance Appliance System 300 is unable to reach a controller, then Assurance Appliance System 300 can determine that the controller has a reachability problem. Assurance Appliance System 300 can determine the reachability problem is based on one or more rules or policies on the network, a hardware problem, and/or a network configuration, and generate a corresponding alert indicating the cause of the reachability problem and/or suggesting a solution to the reachability problem.

If Assurance Appliance System 300 is able to reach a controller and unable to log into the controller, Assurance Appliance System 300 can check the credentials (username and password) used to attempt the login to verify the credentials are correct.

Below is a table illustrating an example of login and reachability conditions for controllers on the network:

TABLE 7 Examples of login and reachability conditions Node IP/Hostname Node DN Reachability (Ping) Login Failure Controller_1 YES YES Controller_2 YES YES Controller_3 YES YES Controller_4 YES YES Controller_5 NO —

Health of Controllers in a Cluster Cannot be Determined

If Assurance Appliance System 300 is unable to determine the health (e.g., reachability, operational status, compatibility, etc.) of controllers in a cluster based on information gathered about those controllers, Assurance Appliance System 300 may stop performing analysis operations (e.g., assurance, troubleshooting, etc.) for a time interval (e.g., epoch) with respect to those controllers and/or information from those controllers.

Controllers are not in Quorum or have No Majority

If controllers are not in quorum or have no majority, then Assurance Appliance System 300 can stop collection operations for information from the controllers and/or stop analysis operations with respect to the controllers and/or information from those controllers.

Controller Quorum or Majority Cannot be Determined and Cluster Health is Unknown

If controller quorum or majority cannot be determined and cluster health is unknown, Assurance Appliance System 300 may not perform collection and/or analysis operations based on the pertinent controllers.

Below is a table illustrating an example of majority and health conditions for controllers on the network:

TABLE 8 Examples of majority and health conditions Operational Status (e.g., as seen on Node IP/Hostname Node DN Majority majority Controllers ) Controller_1 NO Unavailable/ Unregistered/ Available Controller_2 Unknown — Controller_3 Unknown —

Node (e.g., Spine or Leaf) Login Failure

If Assurance Appliance System 300 is unable to login to a node, such as a spine or leaf on the fabric, Assurance Appliance System 300 can determine if the login failure is a result of a reachability problem or credentials, and raise an event indicating the problem causing the login failure.

For example, in response to a failure to login to a switch, Assurance Appliance System 300 can: (1) Determine if the Switch has a valid Out-Of-Band IP address configuration; (2) Determine if HTTPS and SSH are allowed from Assurance Appliance System 300; and/or (3) Determine if the credentials (e.g., username and/or password) are correct or have changed on the switch and take corrective action.

Below is an example table of login and reachability conditions on various switches:

TABLE 9 Examples of login and reachability conditions on switches Node IP Node DN Reachability Login Failure Spine1 YES YES Spine2 YES YES

Collection Failure on a Node

If Assurance Appliance System 300 experiences a problem in a collection (e.g., collection of network models, network configurations, device data, etc.) from a node (e.g., spine or leaf), Assurance Appliance System 300 can raise an event for each node associated with a collection failure. Assurance Appliance System 300 can raise an event indicating a communication error and/or data parsing error, for example, based on a failed attempt to query the node for data.

When a collection from a node, such as a leaf, fails, Assurance Appliance System 300 may not proceed with an analysis operation from the node or may perform a partial analysis.

If one or more queries from Assurance Appliance System 300 to a node fail and the problem is persistent, Assurance Appliance System 300 can collect logs and/or send an alert to a network operator.

Collection Failure on Controller

Assurance Appliance System 300 can raise an event indicating a collection failure from a controller when there is a communication error or data parsing error, for example, associated with that controller after Assurance Appliance System 300 has attempted to query data from the controller. If the problem persists, Assurance Appliance System 300 can collect logs and/or generate an alert for a network operator.

Unsupported Software Version on Controller

If a controller has an unsupported software version, Assurance Appliance System 300 may not perform an analysis associated with that controller for a period of time (e.g., an epoch).

Unsupported Software Version on a Node

If a node has an unsupported software version, Assurance Appliance System 300 may perform a partial analysis. For example, Assurance Appliance System 300 may not perform an analysis for this particular node but may otherwise proceed with an analysis from other nodes having a supported software version.

Analysis Failure

If Assurance Appliance System 300 experiences an internal failure preventing an analysis or collection operation from being executed, Assurance Appliance System 300 can raise an event, such as a failure message, which can indicate the specific component causing the failure.

Partial Analysis Failure

If Assurance Appliance System 300 experiences a partial analysis failure resulting from one or more nodes, Assurance Appliance System 300 identify the one or more nodes causing errors resulting in the partial analysis failure and generate a list of such nodes. For example, Assurance Appliance System 300 may experience a partial failure as data collected from a switch cannot be processed as a result of, for example, truncated data, corrupted data, parsing error, etc., and thereafter identify the nodes associated with such failure. Assurance Appliance System 300 can then generate an alert which can include a list of those nodes causing the partial failure.

Single Controller in Cluster is not Fully Fit

If a single controller in a cluster is not fully fit (e.g., unreachable, unavailable, etc.), Assurance Appliance System 300 can proceed with collection and/or analysis at least for a period of time, such as an epoch. Assurance Appliance System 300 may continue to check if the controller becomes fully fit or remains not fully fit and attempt to address the issue or raise an event.

FIG. 4A illustrates diagram 400 which depicts an example approach for constructing a Logical Model 270 of a network (e.g., Network Environment 100) based on Logical Models 270-1 obtained from various controllers (e.g., Controllers 116-1 through 116-N) on the network. Logical Model 270 will be referenced herein interchangeably as Logical Model 270 or Network-wide Logical Model 270.

Logical Models 270-1 through 270-N can include a respective version of L_Model 270A and/or LR_Model 270B, as shown in FIG. 2D, stored at the respective Controllers 116. Each of the Logical Models 270-1 through 270-N can include objects and configurations of the network stored at the respective Controllers 116. The objects and configurations can include data and configurations provided by the network operator via the Controllers 116. The Controllers 116 can store such objects and configurations to be pushed to the nodes in Fabric 120, such as Leafs 104.

In some cases, the Logical Models 270-1 through 270-N can be obtained from the plurality of controllers by polling the controllers for respective logical models and/or stored configurations. For example, Assurance Appliance System 300 can poll Controllers 116 and extract the logical models and/or configurations from the Controllers 116. Assurance Appliance System 300 can collect the logical models and/or configurations from Controllers 116 via one or more engines or operators (e.g., Operators 310), such as Unified Collector 314 for example. Assurance Appliance System 300 can also collect other data, such as runtime state and/or configurations, from nodes (e.g., Leafs 104) in the network, and incorporate some or all of the information into the Logical Model 270. For example, Assurance Appliance System 300 can collect runtime or state data from the nodes, via for example Topology Explorer 312, and incorporate the runtime or state data into the Logical Model 270.

Assurance Appliance System 300 can collect Logical Models 270-1 through 270-N and generate Logical Model 270 based on Logical Models 270-1 through 270-N. Logical Model 270 can provide a network-wide representation of the network based on the Logical Models 270-1 through 270-N from the Controllers 116. Thus, Logical Model 270 can reflect the intent specification for the network. In other words, Logical Model 270 can reflect the configuration of the network intended by the network operator through the configurations and data specified by the network operator via the Controllers 116.

Logical Model 270 can be generated by combining the Logical Models 270-1 through 270-N. For example, Logical Model 270 can be constructed by comparing the Logical Models 270-1 through 270-N and merging configurations and data from the various logical models into a single logical model. To illustrate, Assurance Appliance System 300 can collect Logical Models 270-1 through 270-N, compare the data in Logical Models 270-1 through 270-N, and construct Logical Model 270 based on the compared data by, for example, merging, combining, and matching portions of the data in Logical Models 270-1 through 270-N.

Logical Model 270 can include the data and/or configurations that are consistently (e.g., matching) including in at least a threshold number of the Logical Models 270-1 through 270-N. For example, the threshold number can be based on whether the logical models with the matching data and/or configurations originated from a number of controllers that is sufficient to establish a quorum, as previously described. In some cases, data and/or configurations only found in logical models originating from a number of controllers that is less than the number necessary for a quorum may be excluded from Logical Model 270. In other cases, such data and/or configurations can be included even if a quorum is not satisfied. For example, such data and/or configurations can be included but verified through subsequent polling of controllers and comparison of logical models. If, after a number of iterations of polling the controllers and comparing the logical models obtained, such data and/or configurations are still not included in the logical models from a quorum of controllers, such data and/or configurations may be discarded, flagged, tested, etc.

In some cases, Logical Model 270 can be periodically updated or verified by polling controllers and analyzing the logical models obtained from the controllers. For example, the controllers can be polled at specific time intervals or scheduled periods. In some cases, the update and/or verification of Logical Model 270 can be triggered by an event, such as a software update, a configuration modification, a network change, etc. For example, the update and/or verification of Logical Model 270 can be triggered when a configuration is modified, added, or removed at one or more controllers. Such event can trigger the polling of controllers for logical models. In some cases, the logical models can be obtained on a push basis such that the controllers can push their logical models and/or configurations periodically and/or based on a triggering event, such as a configuration update.

FIG. 4B illustrates diagram 410 which depicts another example approach for constructing Logical Model 270. In this example, Logical Model 270 is generated from Logical Model Segments 412, 414, 416 obtained from Controllers 116-1 through 116-N on the network (e.g., Network Environment 100). For example, Assurance Appliance System 300 can collect Logical Segments 412, 414, 416 from Controllers 116-1 through 116-N and construct Logical Model 270 based on the collected logical model segments (i.e., Logical Model Segments 412, 414, 416). Logical Model Segments 412, 414, 416 can represent a portion of a respective logical model stored at each of the Controllers 116-1 through 116-N. For example, Controllers 116-1 through 116-N can each store a logical model of the network, which can include the configurations entered at the respective controller by a network operator and/or one or more configurations propagated to the respective controller from other controllers on the network.

The portions of the respective logical models represented by Logical Model Segments 412, 414, 416 can differ based on one or more preferences and represent different aspects of the overall network and/or network-wide logical model or specifications. In some cases, Logical Model Segments 412, 414, 416 can each represent one or more respective elements, configurations, objects, etc., configured on the network (e.g., specified in the logical models on Controllers 116-1 through 116-N), such as one or more respective tenants, VRFs, Domains, EPGs, Services, VLANs, networks, contracts, application profiles, bridge domains, etc.

For example, Logical Model Segment 412 can represent the data and configurations at Controller 116-1 for Tenant A, Logical Model Segment 414 can represent the data and configurations at Controller 116-2 for Tenant B, and Logical Model Segment 416 can represent the data and configurations at Controller 116-N for Tenants C and D. As another example, Logical Model Segment 412 can represent the data and configurations at Controller 116-1 for EPG A, Logical Model Segment 414 can represent the data and configurations at Controller 116-2 for EPG B, and Logical Model Segment 416 can represent the data and configurations at Controller 116-N for EPG C. Together, Logical Model Segments 412, 414, 416 can provide the network-wide data and configurations for the network, which can be used to generate Logical Model 270 representing a network-wide logical model for the network. Thus, Assurance Appliance System 300 can stitch together (e.g., combine, merge, etc.) Logical Model Segments 412, 414, 416 to construct Logical Model 270.

Using Logical Model Segments 412, 414, 416 to construct Logical Model 270, as opposed to the entire copy of the logical models at Controllers 116-1 through 116-N, can in some cases increase performance, reduce network congestion or bandwidth usage, prevent or limit logical model inconsistencies, reduce errors, etc. For example, in a large network, collecting the entire logical models at Controllers 116-1 through 116-N can use a significant amount of bandwidth and create congestion. Moreover, the logical models at Controllers 116-1 through 116-N may contain a significant amount of redundancy which may unnecessarily add extra loads and burden on the network. Thus, Assurance Appliance System 300 can divide the portion(s) of the logical models and data collected from Controllers 116-1 through 116-N into segments, and instead collect the segments of the logical model data from Controllers 116-1 through 116-N, which in this example are represented by Logical Model Segments 412, 414, 416.

In some cases, Assurance Appliance System 300 can determine which controllers to collect data (e.g., logical model segments) from, which data (e.g., logical model segments) to collect from which collectors, and/or which collectors can be verified as reliable, etc. For example, Assurance Appliance System 300 can collect Logical Model Segments 412, 414, 416 from a Cluster 418 of controllers. Cluster 418 can include those controllers that have a specific status or characteristic, such as an active status, a reachable status, a specific software version, a specific hardware version, etc. For example, Cluster 418 may include controllers that are active, have a specific hardware or software version, and/or are reachable by other nodes, such as controllers, in the network, and may exclude any controllers that are not active, do not have a specific hardware or software version, and/or are not reachable by other nodes.

Assurance Appliance System 300 can also determine if the controllers in Cluster 418 (e.g., Controllers 116-1 through 116-N) form a quorum. A quorum determination can be made as previously explained based on one or more quorum rules, for example, a number or ratio of controllers in Cluster 418. If Cluster 418 forms a quorum, Assurance Appliance System 300 may proceed with the collection of Logical Model Segments 412, 414, 416. On the other hand, if Cluster 418 does not form a quorum, Assurance Appliance System 300 can delay the collection, issue an error or notification alert, and/or try to determine if other controllers are available and can be included in Cluster 418 to satisfy the quorum.

In this example, Diagram 410 illustrates a single cluster, Cluster 418. Here, Cluster 418 is provided for clarity and explanation purposes. However, it should be noted that other configurations and examples can include multiple clusters. For example, Controllers 116 can be grouped into different clusters. Assurance Appliance System 300 can collect different information (e.g., logical segments) from the different clusters or may collect the same information from two or more clusters. To illustrate, in some examples, Assurance Appliance System 300 can collect logical segments A-D from a first cluster, logical segments E-G from a second cluster, logical segments H-F from a third cluster, and so forth.

In other examples, Assurance Appliance System 300 can collect logical segments A-D from a first cluster and a second cluster, logical segments E-G from a third cluster and a fourth cluster, logical segments H-F from a fifth cluster and a sixth cluster, and so forth. Here, Assurance Appliance System 300 can collect the same logical segment(s) from two or more different clusters, or distribute the collection of multiple logical segments across two or more clusters. To illustrate, in the previous example, when collecting logical segments A-D from a first cluster and a second cluster, Assurance Appliance System 300 can collect logical segments A-D from the first cluster as well as the second cluster, thus having multiple copies of logical segments A-D (i.e., a copy from the first cluster and a second copy from the second cluster), or otherwise collect logical segments A-B from the first cluster and logical segments C-D from the second cluster, thus distributing the collection of logical segments A-D across the first and second clusters. When collecting a copy of one or more logical segments from different clusters (e.g., a copy of logical segments A-D from the first cluster and a second copy of logical segments A-D from a second cluster), Assurance Appliance System 300 can maintain a copy for redundancy and/or use the additional copy or copies for verification (e.g., accuracy verification), completeness, etc.

In some cases, data and/or configurations (e.g., logical model segments) collected from a cluster having a number of controllers that is less than the number necessary for a quorum, may be excluded from Logical Model 270. In other cases, such data and/or configurations can be included even if a quorum is not satisfied. For example, such data and/or configurations can be included but verified through subsequent polling or monitoring controllers in the cluster and determining a health of the controllers, a quorum state of the cluster, a status of the controllers (e.g., reachability, software or hardware versions, etc.), a reliability of the controllers and/or respective data, etc. If a cluster and/or number of controllers are not in quorum and/or are determined to have a certain condition (e.g., unreachability, error, incompatible software and/or hardware version, etc.), data from such cluster or number of controllers may be excluded from Logical Model 270, discarded, flag, etc., and an error or message notification generated indicating the condition or status associated with the cluster and/or number of controllers.

In some cases, Logical Model 270 can be periodically updated or verified by polling Controllers 116-1 through 116-N and analyzing Logical Model Segments 412, 414, 416 collected from Controllers 116-1 through 116-N in Cluster 418. For example, Controllers 116-1 through 116-N can be polled at specific time intervals or scheduled periods. In some cases, an update and/or verification of Logical Model 270 can be triggered by an event, such as a software update, a configuration modification, a network change, etc. For example, the update and/or verification of Logical Model 270 can be triggered when a configuration is modified, added, or removed at one or more controllers. Such event can trigger Assurance Appliance System 300 to poll Controllers 116-1 through 116-N for Logical Model Segments 412, 414, 416, and/or other information such as runtime data, health data, status data (e.g., connectivity, state, etc.), stored data, updates, etc.

Logical Model Segments 412, 414, 416 can be collected on a push and/or pull basis. For example, Logical Model Segments 412, 414, 416 can be pulled by Assurance Appliance System 300 and/or pushed by Controllers 116-1 through 116-N, periodically and/or based on a triggering event (e.g., an update, an error, network change, etc.).

Logical Model 270 shown in FIGS. 4A and 4B can include runtime state or data from the network and/or nodes, as described with respect to LR_Model 270B. Thus, Logical Model 270 can be a logical model such as L_Model 270A or a logical model with runtime state or data, such as LR-Model 270B. In some cases, Assurance Appliance System 300 can obtain Logical Model 270 and incorporate runtime state or data to generate a runtime, network-wide logical model such as LR-Model 270B. Moreover, Assurance Appliance System 300 can maintain a copy of Logical Model 270 with runtime state or data and without runtime state or data. For example, Assurance Appliance System 300 can maintain a copy of L_Model 270A and a copy of LR_Model 270B.

FIG. 4C illustrates an example diagram 420 for constructing node-specific logical models (e.g., Li_Models 272) based on Logical Model 270 of the network (e.g., Network Environment 100). As previously explained, Logical Model 270 can be a network-wide logical model of the network, and can include runtime data or state as described with respect to LR_Model 270B. In this example, it is assumed that Logical Model 270 includes runtime state or data.

Logical Model 270 can include objects and configurations of the network to be pushed, via for example Controllers 116, to the nodes in Fabric 120, such as Leafs 104. Accordingly, Logical Model 270 can be used to construct a Node-Specific Logical Model (e.g., Li_Model 272) for each of the nodes in Fabric 120 (e.g., Leafs 104). To this end, Logical Model 270 can be adapted for each of the nodes (e.g., Leafs 104) in order to generate a respective logical model for each node, which represents, and/or corresponds to, the portion(s) and/or information from Logical Model 270 that is pertinent to the node, and/or the portion(s) and/or information from Logical Model 270 that should be, and/or is, pushed, stored, and/or rendered at the node.

Each of the Node-Specific Logical Models, Li_Model 272, can contain those objects, properties, configurations, data, etc., from Logical Model 270 that pertain to the specific node, including any portion(s) from Logical Model 270 projected or rendered on the specific node when the network-wide intent specified by Logical Model 270 is propagated or projected to the individual node. In other words, to carry out the intent specified in Logical Model 270, the individual nodes (e.g., Leafs 104) can implement respective portions of Logical Model 270 such that together, the individual nodes can carry out the intent specified in Logical Model 270.

The Node-Specific Logical Models, Li_Model 272, would thus contain the data and/or configurations, including rules and properties, to be rendered by the software at the respective nodes. In other words, the Node-Specific Logical Models, Li_Model 272, includes the data for configuring the specific nodes. The rendered configurations and data at the nodes can then be subsequently pushed to the node hardware (e.g., TCAM), to generate the rendered configurations on the node's hardware.

As used herein, the terms node-specific logical model, device-specific logical model, switch-specific logical model, node-level logical model, device-level logical model, and switch-level logical model can be used interchangeably to refer to the Node-Specific Logical Models and Li_Models 272 as shown in FIGS. 2D and 4B.

FIG. 5A illustrates a schematic diagram of an example system for policy analysis in a network (e.g., Network Environment 100). Policy Analyzer 504 can perform assurance checks to detect configuration violations, logical lint events, contradictory or conflicting policies, unused contracts, incomplete configurations, routing checks, rendering errors, incorrect rules, etc. Policy Analyzer 504 can check the specification of the user's intent or intents in L_Model 270A (or Logical Model 270 as shown in FIG. 4) to determine if any configurations in Controllers 116 are inconsistent with the specification of the user's intent or intents.

Policy Analyzer 504 can include one or more of the Operators 310 executed or hosted in Assurance Appliance System 300. However, in other configurations, Policy Analyzer 504 can run one or more operators or engines that are separate from Operators 310 and/or Assurance Appliance System 300. For example, Policy Analyzer 504 can be implemented via a VM, a software container, a cluster of VMs or software containers, an endpoint, a collection of endpoints, a service function chain, etc., any of which may be separate from Assurance Appliance System 300.

Policy Analyzer 504 can receive as input Logical Model Collection 502, which can include Logical Model 270 as shown in FIG. 4; and/or L_Model 270A, LR_Model 270B, and/or Li_Model 272 as shown in FIG. 2D. Policy Analyzer 504 can also receive as input Rules 508. Rules 508 can be defined, for example, per feature (e.g., per object, per object property, per contract, per rule, etc.) in one or more logical models from the Logical Model Collection 502. Rules 508 can be based on objects, relationships, definitions, configurations, and any other features in MIM 200. Rules 508 can specify conditions, relationships, parameters, and/or any other information for identifying configuration violations or issues.

Rules 508 can include information for identifying syntactic violations or issues. For example, Rules 508 can include one or more statements and/or conditions for performing syntactic checks. Syntactic checks can verify that the configuration of a logical model and/or the Logical Model Collection 502 is complete, and can help identify configurations or rules from the logical model and/or the Logical Model Collection 502 that are not being used. Syntactic checks can also verify that the configurations in the hierarchical MIM 200 have been properly or completely defined in the Logical Model Collection 502, and identify any configurations that are defined but not used. To illustrate, Rules 508 can specify that every tenant defined in the Logical Model Collection 502 should have a context configured; every contract in the Logical Model Collection 502 should specify a provider EPG and a consumer EPG; every contract in the Logical Model Collection 502 should specify a subject, filter, and/or port; etc.

Rules 508 can also include information for performing semantic checks and identifying semantic violations. Semantic checks can check conflicting rules or configurations. For example, Rule1 and Rule2 can overlap and create aliasing issues, Rule1 can be more specific than Rule2 and result in conflicts, Rule1 can mask Rule2 or inadvertently overrule Rule2 based on respective priorities, etc. Thus, Rules 508 can define conditions which may result in aliased rules, conflicting rules, etc. To illustrate, Rules 508 can indicate that an allow policy for a specific communication between two objects may conflict with a deny policy for the same communication between two objects if the allow policy has a higher priority than the deny policy. Rules 508 can indicate that a rule for an object renders another rule unnecessary due to aliasing and/or priorities. As another example, Rules 508 can indicate that a QoS policy in a contract conflicts with a QoS rule stored on a node.

Policy Analyzer 504 can apply Rules 508 to the Logical Model Collection 502 to check configurations in the Logical Model Collection 502 and output Configuration Violation Events 506 (e.g., alerts, logs, notifications, etc.) based on any issues detected. Configuration Violation Events 506 can include semantic or semantic problems, such as incomplete configurations, conflicting configurations, aliased rules, unused configurations, errors, policy violations, misconfigured objects, incomplete configurations, incorrect contract scopes, improper object relationships, etc.

In some cases, Policy Analyzer 504 can iteratively traverse each node in a tree generated based on the Logical Model Collection 502 and/or MIM 200, and apply Rules 508 at each node in the tree to determine if any nodes yield a violation (e.g., incomplete configuration, improper configuration, unused configuration, etc.). Policy Analyzer 504 can output Configuration Violation Events 506 when it detects any violations.

FIG. 5B illustrates an example equivalency diagram 510 of network models. In this example, the Logical Model 270 can be compared with the Hi_Model 276 obtained from one or more Leafs 104 in the Fabric 120. This comparison can provide an equivalency check in order to determine whether the logical configuration of the Network Environment 100 at the Controller(s) 116 is consistent with, or conflicts with, the rules rendered on the one or more Leafs 104 (e.g., rules and/or configurations in storage, such as TCAM). For explanation purposes, Logical Model 270 and Hi_Model 276 are illustrated as the models compared in the equivalency check example in FIG. 5B. However, it should be noted that, in other examples, other models can be checked to perform an equivalency check for those models. For example, an equivalency check can compare Logical Model 270 with Ci_Model 274 and/or Hi_Model 276, Li_Model 272 with Ci_Model 274 and/or Hi_Model 276, Ci_Model 274 with Hi_Model 276, etc.

Equivalency checks can identify whether the network operator's configured intent is consistent with the network's actual behavior, as well as whether information propagated between models and/or devices in the network is consistent, conflicts, contains errors, etc. For example, a network operator can define objects and configurations for Network Environment 100 from Controller(s) 116. Controller(s) 116 can store the definitions and configurations from the network operator and construct a logical model (e.g., L_Model 270A) of the Network Environment 100. The Controller(s) 116 can push the definitions and configurations provided by the network operator and reflected in the logical model to each of the nodes (e.g., Leafs 104) in the Fabric 120. In some cases, the Controller(s) 116 may push a node-specific version of the logical model (e.g., Li_Model 272) that reflects the information in the logical model of the network (e.g., L_Model 270A) pertaining to that node.

The nodes in the Fabric 120 can receive such information and render or compile rules on the node's software (e.g., Operating System). The rules/configurations rendered or compiled on the node's software can be constructed into a Construct Model (e.g., Ci_Model 274). The rules from the Construct Model can then be pushed from the node's software to the node's hardware (e.g., TCAM) and stored or rendered as rules on the node's hardware. The rules stored or rendered on the node's hardware can be constructed into a Hardware Model (e.g., Hi_Model 276) for the node.

The various models (e.g., Logical Model 270 and Hi_Model 276) can thus represent the rules and configurations at each stage (e.g., intent specification at Controller(s) 116, rendering or compiling on the node's software, rendering or storing on the node's hardware, etc.) as the definitions and configurations entered by the network operator are pushed through each stage. Accordingly, an equivalency check of various models, such as Logical Model 270 and Hi_Model 276, Li_Model 272 and Ci_Model 274 or Hi_Model 276, Ci_Model 274 and Hi_Model 276, etc., can be used to determine whether the definitions and configurations have been properly pushed, rendered, and/or stored at any stage associated with the various models.

If the models pass the equivalency check, then the definitions and configurations at checked stage (e.g., Controller(s) 116, software on the node, hardware on the node, etc.) can be verified as accurate and consistent. By contrast, if there is an error in the equivalency check, then a misconfiguration can be detected at one or more specific stages. The equivalency check between various models can also be used to determine where (e.g., at which stage) the problem or misconfiguration has occurred. For example, the stage where the problem or misconfiguration occurred can be ascertained based on which model(s) fail the equivalency check.

The Logical Model 270 and Hi_Model 276 can store or render the rules, configurations, properties, definitions, etc., in a respective structure 512A, 512B. For example, Logical Model 270 can store or render rules, configurations, objects, properties, etc., in a data structure 512A, such as a file or object (e.g., JSON, XML, etc.), and Hi_Model 276 can store or render rules, configurations, etc., in a storage 512B, such as TCAM memory. The structure 512A, 512B associated with Logical Model 270 and Hi_Model 276 can influence the format, organization, type, etc., of the data (e.g., rules, configurations, properties, definitions, etc.) stored or rendered.

For example, Logical Model 270 can store the data as objects and object properties 514A, such as EPGs, contracts, filters, tenants, contexts, BDs, network wide parameters, etc. The Hi_Model 276 can store the data as values and tables 514B, such as value/mask pairs, range expressions, auxiliary tables, etc.

As a result, the data in Logical Model 270 and Hi_Model 276 can be normalized, canonized, diagramed, modeled, re-formatted, flattened, etc., to perform an equivalency between Logical Model 270 and Hi_Model 276. For example, the data can be converted using bit vectors, Boolean functions, ROBDDs, etc., to perform a mathematical check of equivalency between Logical Model 270 and Hi_Model 276.

FIG. 5C illustrates example Architecture 520 for performing equivalence checks of input models. Rather than employing brute force to determine the equivalence of input models, the network models can instead be represented as specific data structures, such as Reduced Ordered Binary Decision Diagrams (ROBDDs) and/or bit vectors. In this example, input models are represented as ROBDDs, where each ROBDD is canonical (unique) to the input rules and their priority ordering.

Each network model is first converted to a flat list of priority ordered rules. In some examples, contracts can be specific to EPGs and thus define communications between EPGs, and rules can be the specific node-to-node implementation of such contracts. Architecture 520 includes a Formal Analysis Engine 522. In some cases, Formal Analysis Engine 522 can be part of Policy Analyzer 504 and/or Assurance Appliance System 300. For example, Formal Analysis Engine 522 can be hosted within, or executed by, Policy Analyzer 504 and/or Assurance Appliance System 300. To illustrate, Formal Analysis Engine 522 can be implemented via one or more operators, VMs, containers, servers, applications, service functions, etc., on Policy Analyzer 504 and/or Assurance Appliance System 300. In other cases, Formal Analysis Engine 522 can be separate from Policy Analyzer 504 and/or Assurance Appliance System 300. For example, Formal Analysis Engine 522 can be a standalone engine, a cluster of engines hosted on multiple systems or networks, a service function chain hosted on one or more systems or networks, a VM, a software container, a cluster of VMs or software containers, a cloud-based service, etc.

Formal Analysis Engine 522 includes an ROBDD Generator 526. ROBDD Generator 526 receives Input 524 including flat lists of priority ordered rules for Models 272, 274, 276 as shown in FIG. 2D. These rules can be represented as Boolean functions, where each rule consists of an action (e.g. Permit, Permit_Log, Deny, Deny_Log) and a set of conditions that will trigger that action (e.g. one or more configurations of traffic, such as a packet source, destination, port, header, QoS policy, priority marking, etc.). For example, a rule might be designed as Permit all traffic on port 80. In some examples, each rule might be an n-bit string with m-fields of key-value pairs. For example, each rule might be a 147 bit string with 13 fields of key-value pairs.

As a simplified example, consider a flat list of the priority ordered rules L1, L2, L3, and L4 in Li_Model 272, where L1 is the highest priority rule and L4 is the lowest priority rule. A given packet is first checked against rule L1. If L1 is triggered, then the packet is handled according to the action contained in rule L1. Otherwise, the packet is then checked against rule L2. If L2 is triggered, then the packet is handled according to the action contained in rule L2. Otherwise, the packet is then checked against rule L3, and so on, until the packet either triggers a rule or reaches the end of the listing of rules.

The ROBDD Generator 526 can calculate one or more ROBDDs for the constituent rules L1-L4 of one or more models. An ROBDD can be generated for each action encoded by the rules L1-L4, or each action that may be encoded by the rules L1-L4, such that there is a one-to-one correspondence between the number of actions and the number of ROBDDs generated. For example, the rules L1-L4 might be used to generate L_Permit_(BDD) L_Permit_Log_(BDD), L_Deny-BDD, and L_Deny_Log_(BDD).

Generally, ROBDD Generator 526 begins its calculation with the highest priority rule of Input 524 in the listing of rules received. Continuing the example of rules L1-L4 in Li_Model 272, ROBDD Generator 526 begins with rule L1. Based on the action specified by rule L1 (e.g. Permit, Permit_Log, Deny, Deny_Log), rule L1 is added to the corresponding ROBDD for that action. Next, rule L2 will be added to the corresponding ROBDD for the action that it specifies. In some examples, a reduced form of L2 can be used, given by L1′L2, with L1′ denoting the inverse of L1. This process is then repeated for rules L3 and L4, which have reduced forms given by (L1+L2)′L3 and (L1+L2+L3)′L4, respectively.

Notably, L_Permit_(BDD) and each of the other action-specific ROBDDs encode the portion of each constituent rule L1, L2, L3, L4 that is not already captured by higher priority rules. That is, L1′L2 represents the portion of rule L2 that does not overlap with rule L1, (L1+L2)′L3 represents the portion of rule L3 that does not overlap with either rules L1 or L2, and (L1+L2+L3)′L4 represents the portion of rule L4 that does not overlap with either rules L1 or L2 or L3. This reduced form can be independent of the action specified by an overlapping or higher priority rule and can be calculated based on the conditions that will cause the higher priority rules to trigger.

ROBDD Generator 526 likewise can generate an ROBDD for each associated action of the remaining models associated with Input 524, such as Ci_Model 274 and Hi_Model 276 in this example, or any other models received by ROBDD Generator 526. From the ROBDDs generated, the formal equivalence of any two or more ROBDDs of models can be checked via Equivalence Checker 528, which builds a conflict ROBDD encoding the areas of conflict between input ROBDDs.

In some examples, the ROBDDs being compared will be associated with the same action. For example, Equivalence Checker 528 can check the formal equivalence of L_Permit_(BDD) against H_Permit_(BDD) by calculating the exclusive disjunction between L_Permit_(BDD) and H_Permit_(BDD). More particularly, L_Permit_(BDD)⊕H_Permit_(BDD) (i.e. L_Permit_(BDD) XOR H_Permit_(BDD)) is calculated, although it is understood that the description below is also applicable to other network models (e.g., Logical Model 270, L_Model 270A, LR_Model 270B, Li_Model 272, Ci_Model 274, Hi_Model 276, etc.) and associated actions (Permit, Permit_Log, Deny, Deny_Log, etc.).

An example calculation is illustrated in FIG. 6A, which depicts a simplified representation of a Permit conflict ROBDD 600 a calculated for L_Permit_(BDD) and H_Permit_(BDD). As illustrated, L_Permit_(BDD) includes a unique portion 602 (shaded) and an overlap 604 (unshaded). Similarly, H_Permit_(BDD) includes a unique portion 606 (shaded) and the same overlap 604.

The Permit conflict ROBDD 600 a includes unique portion 602, which represents the set of packet configurations and network actions that are encompassed within L_Permit_(BDD) but not H_Permit_(BDD) (i.e. calculated as L_Permit_(BDD)·H_Permit_(BDD)′), and unique portion 606, which represents the set of packet configurations and network actions that are encompassed within H_Permit_(BDD) but not L_Permit_(BDD) (i.e. calculated as L_Permit_(BDD)′*H_Permit_(BDD)). Note that the unshaded overlap 604 is not part of Permit conflict ROBDD 600 a.

Conceptually, the full circle illustrating L_Permit_(BDD) (e.g. unique portion 602 and overlap 604) represents the fully enumerated set of packet configurations that are encompassed within, or trigger, the Permit rules encoded by input model Li_Model 272. For example, assume Li_Model 272 contains the rules:

L1: port=[1-3] Permit

L2: port=4 Permit

L3: port=[6-8] Permit

L4: port=9 Deny

where ‘port’ represents the port number of a received packet, then the circle illustrating L_Permit_(BDD) contains the set of all packets with port=[1-3], 4, [6-8] that are permitted. Everything outside of this full circle represents the space of packet conditions and/or actions that are different from those specified by the Permit rules contained in Li_Model 272. For example, rule L4 encodes port=9 Deny and would fall outside of the region carved out by L_Permit_(BDD).

Similarly, the full circle illustrating H_Permit_(BDD) (e.g., unique portion 606 and overlap 604) represents the fully enumerated set of packet configurations and network actions that are encompassed within, or trigger, the Permit rules encoded by the input model Hi_Model 276, which contains the rules and/or configurations rendered in hardware. Assume that Hi_Model 276 contains the rules:

-   -   H1: port=[1-3] Permit     -   H2: port=5 Permit     -   H3: port=[6-8] Deny     -   H4: port=10 Deny_Log         In the comparison between L_Permit_(BDD) and H_Permit_(BDD),         only rules L1 and H1 are equivalent, because they match on both         packet condition and action. L2 and H2 are not equivalent         because even though they specify the same action (Permit), this         action is triggered on a different port number (4 vs. 5). L3 and         H3 are not equivalent because even though they trigger on the         same port number (6-8), they trigger different actions (Permit         vs. Deny). L4 and H4 are not equivalent because they trigger on         a different port number (9 vs. 10) and also trigger different         actions (Deny vs. Deny_Log). As such, overlap 604 contains only         the set of packets that are captured by Permit rules L1 and H1,         i.e., the packets with port=[1-3] that are permitted. Unique         portion 602 contains only the set of packets that are captured         by the Permit rules L2 and L3, while unique portion 606 contains         only the set of packets that are captured by Permit rule H2.         These two unique portions encode conflicts between the packet         conditions upon which Li_Model 272 will trigger a Permit, and         the packet conditions upon which the hardware rendered Hi_Model         276 will trigger a Permit. Consequently, it is these two unique         portions 602 and 606 that make up Permit conflict ROBDD 600 a.         The remaining rules L4, H3, and H4 are not Permit rules and         consequently are not represented in L_Permit_(BDD),         H_Permit_(BDD), or Permit conflict ROBDD 600 a.

In general, the action-specific overlaps between any two models contain the set of packets that will trigger the same action no matter whether the rules of the first model or the rules of the second model are applied, while the action-specific conflict ROBDDs between these same two models contains the set of packets that result in conflicts by way of triggering on a different condition, triggering a different action, or both.

It should be noted that in the example described above with respect to FIG. 6A, Li_Model 272 and Hi_Model 276 are used as example input models for illustration purposes, but other models may be similarly used. For example, in some cases, a conflict ROBDD can be calculated based on Logical Model 270, as shown in FIG. 4, and/or any of the models 270A, 270B, 272, 274, 276, as shown in FIG. 2D.

Moreover, for purposes of clarity in the discussion above, Permit conflict ROBDD 600 a portrays L_Permit_(BDD) and H_Permit_(BDD) as singular entities rather than illustrating the effect of each individual rule. Accordingly, FIGS. 6B and 6C present Permit conflict ROBDDs with individual rules depicted. FIG. 6B presents a Permit conflict ROBDD 600 b taken between the illustrated listing of rules L1, L2, H1, and H2. FIG. 6C presents a Permit conflict ROBDD 600 c that adds rule H3 to Permit conflict ROBDD 600 b. Both Figures maintain the same shading convention introduced in FIG. 6A, wherein a given conflict ROBDD comprises only the shaded regions that are shown.

Turning first to FIG. 6B, illustrated is a Permit conflict ROBDD 600 b that is calculated across a second L_Permit_(BDD) consisting of rules L1 and L2, and a second H_Permit_(BDD) consisting of rules H1 and H2. As illustrated, rules L1 and H1 are identical, and entirely overlap with one another—both rules consists of the overlap 612 and overlap 613. Overlap 612 is common between rules L1 and H1, while overlap 613 is common between rules L1, H1, and L2. For purposes of subsequent explanation, assume that rules L1 and H1 are both defined by port=[1-13] Permit.

Rules L2 and H2 are not identical. Rule L2 consists of overlap 613, unique portion 614, and overlap 616. Rule H2 consists only of overlap 616, as it is contained entirely within the region encompassed by rule L2. For example, rule L2 might be port=[10-20] Permit, whereas rule H2 might be port=[15-17] Permit. Conceptually, this is an example of an error that might be encountered by a network assurance check, wherein an Li_Model 272 rule (e.g., L2) specified by a user intent was incorrectly rendered into a node's memory (e.g., switch TCAM) as an Hi_Model 276 rule (e.g., H2). In particular, the scope of the rendered Hi_Model 276 rule H2 is smaller than the intended scope specified by the user intent contained in L2. For example, such a scenario could arise if a switch TCAM runs out of space, and does not have enough free entries to accommodate a full representation of an Li_Model 272 rule.

Regardless of the cause, this error is detected by the construction of the Permit conflict ROBDD 600 b as L_Permit_(BDD)⊕H_Permit_(BDD), where the results of this calculation are indicated by the shaded unique portion 614. This unique portion 614 represents the set of packet configurations and network actions that are contained within L_Permit_(BDD) but not H_Permit_(BDD). In particular, unique portion 614 is contained within the region encompassed by rule L2 but is not contained within either of the regions encompassed by rules H1 and H2, and specifically comprises the set defined by port=[14,18-20] Permit.

To understand how this is determined, recall that rule L2 is represented by port=[10-20] Permit. Rule H1 carves out the portion of L2 defined by port=[10-13] Permit, which is represented as overlap 613. Rule H2 carves out the portion of L2 defined by port=[15-17] Permit, which is represented as overlap 616. This leaves only port=[14,18-20] Permit as the non-overlap portion of the region encompassed by L2, or in other words, the unique portion 614 comprises Permit conflict ROBDD 600 b.

FIG. 6C illustrates a Permit conflict ROBDD 600 c which is identical to Permit conflict ROBDD 600 b with the exception of a newly added third rule, H3: port=[19-25] Permit. Rule H3 includes an overlap portion 628, which represents the set of conditions and actions that are contained in both rules H3 and L2, and further consists of a unique portion 626, which represents the set of conditions and actions that are contained only in rule H3. Conceptually, this could represent an error wherein an Li_Model 272 rule (e.g., L2) specified by a user intent was incorrectly rendered into node memory as two Hi_Model 276 rules (e.g., H2 and H3). There is no inherent fault with a single Li_Model 272 rule being represented as multiple Hi_Model 276 rules. Rather, the fault herein lies in the fact that the two corresponding Hi_Model 276 rules do not adequately capture the full extent of the set of packet configurations encompassed by Permit rule L2. Rule H2 is too narrow in comparison to rule L2, as discussed above with respect to FIG. 6B, and rule H3 is both too narrow and improperly extended beyond the boundary of the region encompasses by rule L2.

As was the case before, this error is detected by the construction of the conflict ROBDD 600 c, as L_Permit_(BDD)⊕H_Permit_(BDD), where the results of this calculation are indicated by the shaded unique portion 624, representing the set of packet configurations and network actions that are contained within L_Permit_(BDD) but not H_Permit_(BDD), and the shaded unique portion 626, representing the set of packet configurations and network actions that are contained within H_Permit_(BDD) but not L_Permit_(BDD). In particular, unique portion 624 is contained only within rule L2, and comprises the set defined by port=[14, 18] Permit, while unique portion 626 is contained only within rule H3, and comprises the set defined by port=[21-25] Permit. Thus, Permit conflict ROBDD 600 c comprises the set defined by port=[14, 18, 21-25] Permit.

Reference is made above only to Permit conflict ROBDDs, although it is understood that conflict ROBDDs are generated for each action associated with a given model. For example, a complete analysis of the Li_Model 272 and Hi_Model 276 mentioned above might entail using ROBDD Generator 526 to generate the eight ROBDDs L_Permit_(BDD), L_Permit_Log_(BDD), L_Deny_(BDD), and L_Deny_Log_(BDD), H_Permit_(BDD), H_Permit_Log_(BDD), H_Deny_(BDD), and H_Deny_Log_(BDD), and then using Equivalence Checker 528 to generate a Permit conflict ROBDD, Permit_Log conflict ROBDD, Deny conflict ROBDD, and Deny_Log conflict ROBDD.

In general, Equivalence Checker 528 generates action-specific conflict ROBDDs based on input network models, or input ROBDDs from ROBDD Generator 526. As illustrated in FIG. 5C, Equivalence Checker 528 receives the input pairs (L_(BDD), H_(BDD)), (L_(BDD), C_(BDD)), (C_(BDD), H_(BDD)), although it is understood that these representations are for clarity purposes, and may be replaced with any of the action-specific ROBDDs discussed above. From these action-specific conflict ROBDDs, Equivalence Checker 528 may determine that there is no conflict between the inputs—that is, a given action-specific conflict ROBDD is empty. In the context of the examples of FIGS. 6A-6C, an empty conflict ROBDD would correspond to no shaded portions being present. In the case where this determination is made for the given action-specific conflict ROBDD, Equivalence Checker 528 might generate a corresponding action-specific “PASS” indication 530 that can be transmitted externally from formal analysis engine 522.

However, if Equivalence Checker 528 determines that there is a conflict between the inputs, and that a given action-specific conflict ROBDD is not empty, then Equivalence Checker 528 will not generate PASS indication 530, and can instead transmit the given action-specific conflict ROBDD 532 to a Conflict Rules Identifier 534, which identifies the specific conflict rules that are present. In some examples, an action-specific “PASS” indication 530 can be generated for every action-specific conflict ROBDD that is determined to be empty. In some examples, the “PASS” indication 530 might only be generated and/or transmitted once every action-specific conflict ROBDD has been determined to be empty.

In instances where one or more action-specific conflict ROBDDs are received, Conflict Rules Identifier 534 may also receive as input the flat listing of priority ordered rules that are represented in each of the conflict ROBDDs 532. For example, if Conflict Rules Identifier 534 receives the Permit conflict ROBDD corresponding to L_Permit_(BDD)⊕H_Permit_(BDD), the underlying flat listings of priority ordered rules Li, Hi used to generate L_Permit_(BDD) and H_Permit_(BDD) are also received as input.

The Conflict Rules Identifier 534 then identifies specific conflict rules from each listing of priority ordered rules and builds a listing of conflict rules 536. In order to do so, Conflict Rules Identifier 534 iterates through the rules contained within a given listing and calculates the intersection between the set of packet configurations and network actions that is encompassed by each given rule, and the set that is encompassed by the action-specific conflict ROBDD. For example, assume that a list of j rules was used to generate L_Permit_(BDD). For each rule j, Conflict Rules Identifier 534 computes: (L_Permit_(BDD) ⊕H_Permit_(BDD))*L _(j) If this calculation equals zero, then the given rule L_(j) is not part of the conflict ROBDD and therefore is not a conflict rule. If, however, this calculation does not equal zero, then the given rule L_(j) is part of the Permit conflict ROBDD and therefore is a conflict rule that is added to the listing of conflict rules 536.

For example, in FIG. 6C, Permit conflict ROBDD 600 c includes the shaded portions 624 and 626. Starting with the two rules L1, L2 used to generate L_Permit_(BDD), it can be calculated that: (L_Permit_(BDD) ⊕H_Permit_(BDD))*L1=0 Thus, rule L1 does not overlap with Permit conflict ROBDD 600 c and therefore is not a conflict rule. However, it can be calculated that: (L_Permit_(BDD) ⊕H_Permit_(BDD))*L2≠0 Meaning that rule L2 does overlap with Permit conflict ROBDD 600 c at overlap portion 624 and therefore is a conflict rule and is added to the listing of conflict rules 536.

The same form of computation can also be applied to the list of rules H1, H2, H3, used to generate H_Permit_(BDD). It can be calculated that: (L_Permit_(BDD) ⊕H_Permit_(BDD))*H1=0 Thus, rule H1 does not overlap with Permit conflict ROBDD 600 c and therefore is not a conflict rule. It can also be calculated that: (L_Permit_(BDD) ⊕H_Permit_(BDD))*H2=0 Thus, rule H2 does not overlap with Permit conflict ROBDD 600 c and therefore is not a conflict rule. Finally, it can be calculated that: (L_Permit_(BDD) ⊕H_Permit_(BDD))*H3≠0 Meaning that rule H2 does overlap with Permit conflict ROBDD 600 c at overlap portion 626 and therefore is a conflict rule and can be added to the listing of conflict rules 552. In the context of the present example, the complete listing of conflict rules 536 derived from Permit conflict ROBDD 600 c is {L2, H3}, as one or both of these rules have been configured or rendered incorrectly.

In some examples, one of the models associated with the Input 524 may be treated as a reference or standard, meaning that the rules contained within that model are assumed to be correct. As such, Conflict Rules Identifier 536 only needs to compute the intersection of a given action-specific conflict ROBDD and the set of associated action-specific rules from the non-reference model. For example, the Li_Model 272 can be treated as a reference or standard, because it is directly derived from user inputs used to define L_Model 270A, 270B. The Hi_Model 276, on the other hand, passes through several transformations before being rendered into a node's hardware, and is therefore more likely to be subject to error. Accordingly, the Conflict Rules Identifier 534 would only compute (L_Permit_(BDD) ⊕H_Permit_(BDD))*H _(j) for each of the rules (or each of the Permit rules) j in the Hi_Model 276, which can cut the required computation time significantly.

Additionally, Conflict Rules Identifier 534 need not calculate the intersection of the action-specific conflict ROBDD and the entirety of each rule, but instead, can use a priority-reduced form of each rule. In other words, this is the form in which the rule is represented within the ROBDD. For example, the priority reduced form of rule H2 is H1′H2, or the contribution of rule H2 minus the portion that is already captured by rule H1. The priority reduced form of rule H3 is (H1+H2)′H3, or the contribution of rule H3 minus the portion that is already captured by rules H1 or H2. The priority reduced form of rule H4 is (H1+H2+H3)′H4, or the contribution of rule H4 minus the portion that is already captured by rules H1 and H2 and H3.

As such, the calculation instead reduces to: (L_Permit_(BDD) ⊕H_Permit_(BDD))*(H1+ . . . +H _(j-1))′H _(j) for each rule (or each Permit rule) j that is contained in the Hi_Model 276. While there are additional terms introduced in the equation above as compared to simply calculating (L_Permit_(BDD) ⊕H_Permit_(BDD))*H _(j), the priority-reduced form is in fact computationally more efficient. For each rule j, the priority-reduced form (H1+ . . . +H_(j-1))′H_(j) encompasses a smaller set of packet configurations and network actions, or encompasses an equally sized set, as compared to the non-reduced form H_(j). The smaller the set for which the intersection calculation is performed against the conflict ROBDD, the more efficient the computation.

In some cases, the Conflict Rules Identifier 534 can output a listing of conflict rules 536 (whether generated from both input models, or generated only a single, non-reference input model) to a destination external to Formal Analysis Engine 522. For example, the conflict rules 536 can be output to a user or network operator in order to better understand the specific reason that a conflict occurred between models.

In some examples, a Back Annotator 538 can be disposed between Conflict Rules Identifier 534 and the external output. Back Annotator 538 can associate each given rule from the conflict rules listing 536 with the specific parent contract or other high-level intent that led to the given rule being generated. In this manner, not only is a formal equivalence failure explained to a user in terms of the specific rules that are in conflict, the equivalence failure is also explained to the user in terms of the high-level user action, configuration, or intent that was entered into the network and ultimately created the conflict rule. In this manner, a user can more effectively address conflict rules, by adjusting or otherwise targeting them at their source or parent.

In some examples, the listing of conflict rules 536 may be maintained and/or transmitted internally to Formal Analysis Engine 522, in order to enable further network assurance analyses and operations such as, without limitation, event generation, counter-example generation, policy assurance, etc.

The disclosure now turns to FIGS. 7A, 7B, and 7C, which illustrate example methods. FIG. 7A illustrates an example method for network assurance, and FIGS. 7B and 7C illustrate example methods for handling failure or error scenarios. The methods are provided by way of example, as there are a variety of ways to carry out the methods. Additionally, while the example methods are illustrated with a particular order of blocks or steps, those of ordinary skill in the art will appreciate that FIGS. 7A-C, and the blocks shown therein, can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIGS. 7A-C represents one or more steps, processes, methods or routines in the methods. For the sake of clarity and explanation purposes, the blocks in FIGS. 7A-C are described with reference to Network Environment 100, Assurance Appliance System 300, Assurance Architecture 350, Network Models 270, 270A-B, 272, 274, 276, Policy Analyzer 504, and Formal Equivalence Engine 522, as shown in FIGS. 1A-B, 2D, 3A-3B, 4A-4B, 5A, and 5C.

With reference to FIG. 7A, at step 700, Assurance Appliance System 300 can collect data and obtain models associated with Network Environment 100. The models can include Logical Model 270, as shown in FIG. 4, and/or any of Models 270A-B, 272, 274, 276, as shown in FIG. 2D. The data can include fabric data (e.g., topology, switch, interface policies, application policies, etc.), network configurations (e.g., BDs, VRFs, L2 Outs, L3 Outs, protocol configurations, etc.), QoS policies (e.g., DSCP, priorities, bandwidth, queuing, transfer rates, SLA rules, performance settings, etc.), security configurations (e.g., contracts, filters, etc.), application policies (e.g., EPG contracts, application profile settings, application priority, etc.), service chaining configurations, routing configurations, etc. Other non-limiting examples of information collected or obtained can include network data (e.g., RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF, ARP, VPC, LLDP, MTU, network or flow state, logs, node information, routes, etc.), rules and tables (e.g., TCAM rules, ECMP tables, routing tables, etc.), endpoint dynamics (e.g., EPM, COOP EP DB, etc.), statistics (e.g., TCAM rule hits, interface counters, bandwidth, packets, application usage, resource usage patterns, error rates, latency, dropped packets, etc.).

At step 702, Assurance Appliance System 300 can analyze and model the received data and models. For example, Assurance Appliance System 300 can perform formal modeling and analysis, which can involve determining equivalency between models, including configurations, policies, etc. Assurance Appliance System 300 can analyze and/or model some or all portions of the received data and models. For example, in some cases, Assurance Appliance System 300 may analyze and model contracts, policies, rules, and state data, but exclude other portions of information collected or available.

At step 704, Assurance Appliance System 300 can generate one or more smart events. Assurance Appliance System 300 can generate smart events using deep object hierarchy for detailed analysis, such as Tenants, switches, VRFs, rules, filters, routes, prefixes, ports, contracts, subjects, etc.

At step 706, Assurance Appliance System 300 can visualize the smart events, analysis and/or models. Assurance Appliance System 300 can display problems and alerts for analysis and debugging, in a user-friendly GUI.

FIG. 7B illustrates a first example method for handling error scenarios during data collection. At step 720, Assurance Appliance System 300 probes a plurality of controllers (e.g., Controllers 116) in a network (e.g., Network Environment 100) for respective status information. The status information can include a reachability status, a software version, a controller version, a current connectivity, a configuration status, etc.

Based on the respective status information, at step 722, Assurance Appliance System 300 identifies a number of controllers from the plurality of controllers having a reachable status and a supported controller version. The reachable status can indicate that an associated controller is reachable by Assurance Appliance System 300. For example, the reachable status can indicate that Assurance Appliance System 300 can communicate with the associated controller. The supported controller version can be based on the controller models, software and/or hardware versions supported by Assurance Appliance System 300. For example, Assurance Appliance System 300 may support one or more controller models, software versions, etc. Based on the supported controller models, software versions, etc., Assurance Appliance System 300 can determine if a controller is a supported controller version.

At step 724, Assurance Appliance System 300 determines whether the number of controllers having the reachable status and the supported controller version form a quorum. Assurance Appliance System 300 can determine if the controllers form a quorum based on one or more quorum rules. The quorum rules may define a number of controllers necessary for a quorum. The number of controllers defined in a quorum rule can be relative to the total number of controllers in the network, such as a ratio or proportional number, or a predetermined number or threshold, such as 10 or 20. For example, a quorum rule may define a quorum as a simple majority or a different ratio such as ⅔ or ¾.

The quorum rules can vary in different configurations and may increase or decrease the threshold for a quorum as desired based on one or more factors or goals, such as accuracy, network size, etc. The quorum rules may also include rules defining a range or minimum or other conditions. For example, the quorum rules may define a majority as the number necessary for a quorum but may include other additional conditions. To illustrate, the quorum rules may define a quorum as a majority but may include a rule specifying a minimum of x number of controllers. Thus, the quorum rules may prevent a quorum when a majority is met if the majority does not amount to at least the minimum of x number of controllers.

At step 726, when the number of controllers is a quorum, Assurance Appliance System 300 collects a respective logical model of the network (e.g., Logical Model 270) from each controller in the quorum. For example, if 20 controllers are reachable and supported, and the 20 controllers are sufficient to form a quorum, then Assurance Appliance System 300 can collect a logical model from each of the 20 controllers. Assurance Appliance System 300 can also collect other data from the controllers, such as state information, statistics, and other network information.

At step 728, based on the respective logical model collected from each controller in the quorum, Assurance Appliance System 300 determines respective node information for each node (e.g., Spines 102 and/or Leafs 104) in a fabric (e.g., Fabric 120) of the network. For example, Assurance Appliance System 300 can analyze the logical models collected and identify the nodes in the fabric, as well information about each node such as a software version of the node, a hardware version of the node (e.g., TCAM version and/or device model), configuration information, status information, connectivity information, model information, firmware information, etc.

In some cases, Assurance Appliance System 300 can identify the nodes in the fabric based on the collected logical models, and poll the nodes to extract or obtain respective information from the nodes, such as software, hardware, model, versioning, and status information. In some cases, Assurance Appliance System 300 can identify the nodes and the respective information from the collected logical models, the nodes, and/or one or more other devices or locations, such as controllers.

At step 730, Assurance Appliance System 300 determines, based on the respective node information, a list of nodes in the fabric to include in one or more assurance analysis operations. For example, Assurance Appliance System 300 can generate a list of nodes that have a specific software version, such as a supported software version, a specific hardware version, a matching software and/or hardware version, a specific status (e.g., reachable, registered, active, inactive, disabled, undiscovered, discovering, unknown, etc.), etc.

In some cases, the list of nodes can be based on one or more specific node states and/or characteristics, which can be determined based on the respective node information. For example, the list can include nodes that are reachable, active, inactive, registered, supported, matching (e.g., same software, same hardware, same model, etc.), etc. Assurance Appliance System 300 can exclude from the list nodes that do not have the specific node states and/or characteristics. For example, Assurance Appliance System 300 can exclude nodes that are not reachable and/or supported, nodes that are disabled, nodes that are not registered, nodes that are unknown, nodes that are undiscovered, nodes that are in a discovering state, nodes that have a different characteristic (e.g., software version, hardware version, model, etc.), nodes that lack connectivity, etc.

Assurance Appliance System 300 can use the list to determine which nodes to include in the assurance analysis operations. The assurance analysis operations can include data collection operations, data analysis operations, data equivalency operations, data validity operations, etc. For example, the assurance analysis operations can include a collection and analysis operations, which includes collection of data from the nodes (e.g., models, configurations, state, rendered data, etc.) and analysis of data from the nodes, such as model equivalency operations. To illustrate, the list can dictate which nodes to collect data from, which nodes to construct respective logical models for (e.g., node-level models), which nodes to obtain concrete models (e.g., Ci_Model 274) from, which nodes to obtain hardware models (Hi_Model 276 from), which nodes to obtain stored data from, which nodes to poll, which nodes to perform equivalence checks for between node-specific models (e.g., Ci_Model 274 and Hi_Model 276), etc.

The list and respective node information can also be used to determine which of various assurance analysis operations to perform for a particular node in the list. For example, Assurance Appliance System 300 can obtain a concrete model (e.g., Ci_Model 274) for each node in the list, but only obtain a hardware model (Hi_Model 276) for particular nodes within the list, such as nodes having a supported hardware version. Assurance Appliance System 300 can determine which models are obtained for which nodes, and perform equivalence checks between models based on the models obtained for the nodes. For example, Assurance Appliance System 300 can determine whether a concrete and/or a hardware model has been obtained for a node. Assurance Appliance System 300 can then compare the logical model, concrete model, and/or hardware model for each node, depending on which models have been obtained for that node, to verify configurations at various levels for that node, including logical configurations defined in the logical model, configurations rendered in software based on the configurations in the logical model, configurations rendered in hardware from the configurations rendered in software, etc.

Assurance Appliance System 300 can poll and/or parse data from nodes in the list. Assurance Appliance System 300 can also determine if a parsing error or failure has occurred for a particular node. Assurance Appliance System 300 can determine if the parsing error is a partial or complete parsing error, and generate an alert based on the parsing error. Assurance Appliance System 300 can also determine whether to continue to analyze or parse data from a node based on whether the parsing error is a partial or complete parsing error. For example, Assurance Appliance System 300 can continue to analyze and/or poll data from a node if the parsing error for that node is a partial parsing error.

Assurance Appliance System 300 can generate events and responses to failures and conditions identified at any of steps 720-730, as previously described with respect to FIG. 3B. For example, Assurance Appliance System 300 can determine which nodes and controllers are reachable, unreachable, supported, and/or unsupported, and generate an alert and response, such a collection or analysis decision, based on whether a specific node(s) or controller(s) is reachable, unreachable, unsupported, supported, etc. To illustrate, Assurance Appliance System 300 can determine what, if any, data to collect and/or analyze; what, if any, events to generate; etc.; based on whether a node or controller is reachable, supported, etc.

Assurance Appliance System 300 can also make collection and/or analysis decisions for nodes and controllers based on other factors, such as status conditions and device characteristics. For example, Assurance Appliance System 300 can make data collection and/or analysis decisions based on a node or controller's software version, hardware version, model, connectivity, registration status, configuration state, data compatibility, etc. Moreover, Assurance Appliance System 300 can generate events on such factors, which can notify the network and/or network operator of various conditions of the network, nodes, and/or controllers, such as compatibility, versions, status, errors, etc.

FIG. 7C illustrates a second example method for handling error scenarios. At step 740, Assurance Appliance System 300 obtains a logical model (e.g., Logical Model 270) of a network (e.g., Network Environment 100). Based on the logical model, at step 742, Assurance Appliance System 300 identifies a plurality of controllers (e.g., Controllers 116) on the network and a plurality of nodes (e.g., Spines 102, Leafs 104) on a fabric (e.g., Fabric 120) of the network. For example, Assurance Appliance System 300 can identify the controllers and nodes on the network as well as information about the controllers and nodes, such as names (e.g., hostnames, distinguished names, network names, etc.), addresses (e.g., IP addresses, MAC addresses, virtual IP addresses, etc.), device identifiers, network identifiers, etc. Assurance Appliance System 300 can obtain such information about the controllers and nodes on the network based on the logical model. For example, Assurance Appliance System 300 can identify the names and addresses of controllers and nodes configured on the logical model for the network based on the data and configurations defined in the logical model.

At step 744, Assurance Appliance System 300 probes the plurality of controllers and the plurality of nodes for respective status information. For example, Assurance Appliance System 300 can ping the controllers and nodes, query the controllers and nodes, message the controllers and nodes, etc. The respective status information can include, without limitation, respective reachability information, respective login diagnostics information, respective software information, respective availability information, respective state information, etc. For example, the respective status information can indicate whether a respective controller or node is reachable from Assurance Appliance System 300, whether the respective controller or node is available, whether the respective controller or node is registered, whether the respective controller or node is running a compatible software version, whether Assurance Appliance System 300 is able to login to the respective controller or node, whether the respective controller or node has experienced a problem (e.g., a failure such as a collection failure or an analysis failure), whether a health of the respective controller or node is known or available, whether a quorum status of one or more controllers is known or available, whether a particular number of controllers or nodes are fit or have experienced a problem, etc.

Based on the respective status information, at step 746, Assurance Appliance System 300 determines conditions at the plurality of controllers and the plurality of nodes. For example, Assurance Appliance System 300 can determine a health, software compatibility, reachability, login or authentication ability, a failure or error, a quorum state, an availability, a registration state, a responsiveness, etc.

At step 748, Assurance Appliance System 300 defines one or more assurance operations based on the conditions at the plurality of controllers and the plurality of nodes. Defining one or more assurance operations can include deciding to start, stop, pause, or continue specific operations. Moreover, defining one or more assurance operations can include deciding whether to use information from one or more controllers or nodes for the particular operations, or deciding whether to perform the particular operations with respect to one or more controllers or nodes.

For example, Assurance Appliance System 300 can decide to stop collection or analysis operations for one or more specific controllers or nodes having a particular condition, such as a failure, an unreachability status, a login error, an unknown health, an incompatible software version, an unknown quorum state, etc. To illustrate, Assurance Appliance System 300 can stop collection operations for nodes having an incompatible software version and stop using data for analysis operations from controllers that are not in quorum.

The specific operations can include, for example, collection operations, analysis operations, troubleshooting operations, error handling operations, error generation operations, etc. Moreover, when deciding to start, stop, pause, etc., particular operations, Assurance Appliance System 300 can decide to start, stop, pause, or continue the particular operations for a specific period of time or interval, such as an epoch, or until a particular event is identified, such as a failure correction or status change.

At step 750, Assurance Appliance System 300 performs the one or more assurance operations. Performing the one or more assurance operations can include starting, stopping, pausing, withholding, continuing, etc., particular operations. Moreover, performing the one or more assurance operations can include excluding data from particular nodes or controllers when performing particular operations, such as collection or analysis operations. The operations or use of data for operations can be defined for all controllers or nodes or a particular set of controllers or nodes, such as a set of controllers or nodes having a specific state or status (e.g., a failure).

The one or more assurance operations can include responses, operations, and/or decisions based on specific events. Non-limiting examples of specific events can include an indication of a controller login failure, a health of controllers in a cluster cannot be determined, controllers are not in quorum or have no majority, a node (e.g., spine or leaf) login failure, a collection failure on a node, a collection failure on a controller, an unsupported software version on a controller, an unsupported software version on a node, an analysis failure, a partial analysis failure, a controller in a cluster is not fit, etc., as previously described.

In some examples, the one or more assurance operations can include responding to error events, such as a login or authentication event. For example, responding to a login error can include verifying login credentials, verifying network rules associated with network protocols used to login or authenticate (e.g., HTTPS, SSH, etc.), verifying network connectivity, and so forth.

In some examples, defining one or more assurance operations can include stopping collection or analysis operations when a threshold number of controllers have a particular condition, such as an unknown health status, an unavailability status, an unknown quorum state, an unreachability status, a login failure, etc. The threshold number of controllers can be a majority of controllers or a quorum of controllers, for example.

In some examples, defining one or more assurance operations can include stopping collection or analysis operations associated with one or more nodes when the one or more nodes have a particular condition, such as a login failure, an unreachability status, a collection failure, an incompatible software version. For example, defining one or more assurance operations can include stopping collection of data from, or use of data from, one or more nodes that have an incompatible software version, are unreachable, or have a particular error (e.g., parsing error, login error, data integrity error, etc.).

The disclosure now turns to FIGS. 8 and 9, which illustrate example network and computing devices, such as switches, routers, load balancers, servers, client computers, and so forth.

FIG. 8 illustrates an example network device 800 suitable for performing switching, routing, assurance, and other networking operations. Network device 800 includes a central processing unit (CPU) 804, interfaces 802, and a connection 810 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU 804 is responsible for executing packet management, error detection, and/or routing functions. The CPU 804 preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU 804 may include one or more processors 808, such as a processor from the INTEL X86 family of microprocessors. In some cases, processor 808 can be specially designed hardware for controlling the operations of network device 800. In some cases, a memory 806 (e.g., non-volatile RAM, ROM, TCAM, etc.) also forms part of CPU 804. However, there are many different ways in which memory could be coupled to the system. In some cases, the network device 800 can include a memory and/or storage hardware, such as TCAM, separate from CPU 804. Such memory and/or storage hardware can be coupled with the network device 800 and its components via, for example, connection 810.

The interfaces 802 are typically provided as modular interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over the network and sometimes support other peripherals used with the network device 800. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast token ring interfaces, wireless interfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5G cellular interfaces, CAN BUS, LoRA, and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control, signal processing, crypto processing, and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor 804 to efficiently perform routing computations, network diagnostics, security functions, etc.

Although the system shown in FIG. 8 is one specific network device of the present disclosure, it is by no means the only network device architecture on which the concepts herein can be implemented. For example, an architecture having a single processor that handles communications as well as routing computations, etc., can be used. Further, other types of interfaces and media could also be used with the network device 800.

Regardless of the network device's configuration, it may employ one or more memories or memory modules (including memory 806) configured to store program instructions for the general-purpose network operations and mechanisms for roaming, route optimization and routing functions described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store tables such as mobility binding, registration, and association tables, etc. Memory 806 could also hold various software containers and virtualized execution environments and data.

The network device 800 can also include an application-specific integrated circuit (ASIC), which can be configured to perform routing, switching, and/or other operations. The ASIC can communicate with other components in the network device 800 via the connection 810, to exchange data and signals and coordinate various types of operations by the network device 800, such as routing, switching, and/or data storage operations, for example.

FIG. 9 illustrates a computing system architecture 900 including components in electrical communication with each other using a connection 905, such as a bus. System 900 includes a processing unit (CPU or processor) 910 and a system connection 905 that couples various system components including the system memory 915, such as read only memory (ROM) 920 and random access memory (RAM) 925, to the processor 910. The system 900 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 910. The system 900 can copy data from the memory 915 and/or the storage device 930 to the cache 912 for quick access by the processor 910. In this way, the cache can provide a performance boost that avoids processor 910 delays while waiting for data. These and other modules can control or be configured to control the processor 910 to perform various actions. Other system memory 915 may be available for use as well. The memory 915 can include multiple different types of memory with different performance characteristics. The processor 910 can include any general purpose processor and a hardware or software service, such as service 1 932, service 2 934, and service 3 936 stored in storage device 930, configured to control the processor 910 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 910 may be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction with the computing device 900, an input device 945 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 935 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 900. The communications interface 940 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 930 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 925, read only memory (ROM) 920, and hybrids thereof.

The storage device 930 can include services 932, 934, 936 for controlling the processor 910. Other hardware or software modules are contemplated. The storage device 930 can be connected to the system connection 905. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 910, connection 905, output device 935, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of a set and indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B. 

What is claimed is:
 1. A method comprising: obtaining a global logical model of a network; the global logical model identifying how a plurality of controllers and a plurality of nodes on a fabric of the network communicate; probing, by a network device, the plurality of controllers for respective controller status information, the respective controller status information comprising at least one of respective reachability information, respective login diagnostics information, and respective software information; first determining, based on the respective controller status information, whether a quorum of the plurality of controllers are reachable and have a supported controller version; retrieving, in response to a positive result of the first determining, a local logical model from each of the controllers in the quorum, the local logical model, being device specific instructions from the global logical model that are specific to operability of each of the controllers of the quorum; second determining, from the retrieved local logical models of the controllers in the quorum, respective node status information for the plurality of nodes; generating, based on the respective node status information, a list of nodes to include in one or more assurance operations performing the one or more assurance operations on the nodes of the list.
 2. The method of claim 1, wherein the respective network connectivity status comprises a reachability from a network device to each respective controller and each respective node, wherein the respective login ability comprises an ability to login from the network device to each respective controller and each respective node, wherein the respective software compatibility is based on a respective software version associated with each respective controller and each respective node.
 3. The method of claim 2, wherein the respective login diagnostics information comprises at least one of a first indication of a response to a login request or a second indication of an authentication status, wherein the response to the login request comprises at least one of a login failure or a login success, and wherein the authentication status comprises at least one of authenticated or unauthenticated.
 4. The method of claim 2, wherein the one or more assurance operations comprise at least one of collecting configuration data, analyzing network health, generating error events, or responding to error events.
 5. The method of claim 4, wherein the one or more assurance operations comprise responding to error events, wherein the error events comprise at least one of a login event or an authentication event, wherein responding to error events comprises at least one of verifying login credentials, verifying network rules associated with network protocols used to login or authenticate.
 6. The method of claim 4, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations when at least a threshold number of controllers have a particular condition comprising at least one of an unknown health status, an unavailability status, an unknown quorum state, an unreachability the network device, or a problem authenticating the network device.
 7. The method of claim 6, wherein the threshold number comprises at least one of a majority of controllers or a quorum of controllers.
 8. The method of claim 7, further comprising stopping the at least one of collection or analysis operations only with respect to controllers from the threshold number of controllers.
 9. The method of claim 4, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations associated with one or more nodes when the one or more nodes have a particular condition comprising at least one of a login failure, an unreachability status, a collection failure, or an incompatible software version.
 10. A system comprising: one or more processors; and at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the system to: obtain a global logical model of a network, the global logical model identifying how a plurality of controllers and a plurality of nodes on a fabric of the network communicate; probe the plurality of controllers for respective status information, the respective status information comprising at least one of respective reachability information, respective login diagnostics information, and respective software information; first determine, based on the respective controller status information, whether a quorum of the plurality of controllers are reachable and have a supported controller version; retrieve, in response to a positive result of the first determine, a local logical model from each of the controllers in the quorum, the local logical model, being device specific instructions from the global logical model that are specific to operability of each of the controllers of the quorum; second determine, from the retrieved local logical models of the controllers in the quorum, respective node status information for the plurality of nodes; define generate, based on the respective node status information, a list of nodes to include in one or more assurance operations; and perform the one or more assurance operations on the nodes of the list.
 11. The system of claim 10, wherein the one or more assurance operations comprise at least one of collecting configuration data, analyzing network health, generating error events, or responding to error events.
 12. The system of claim 11, wherein the one or more assurance operations comprise responding to error events, wherein the error events comprise at least one of a login event or an authentication event, wherein responding to error events comprises at least one of verifying login credentials, verifying network rules associated with network protocols used to login or authenticate.
 13. The system of claim 11, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations when at least a threshold number of controllers have a particular condition collection failure, or an incompatible software version.
 14. The system of claim 11, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations associated with one or more nodes when the one or more nodes have a particular condition comprising at least one of a login failure, an unreachability status, a perform the one or more assurance operations on the nodes of the list.
 15. A non-transitory computer-readable storage medium having stored therein instructions which, when executed by one or more processors, cause the one or more processors to: obtain a global logical model of a network the global logical model identifying how a plurality of controllers and a plurality of nodes on a fabric of the network communicate; probe the plurality of controllers for respective status information, the respective status information comprising at least one of respective reachability information, respective login diagnostics information, and respective software information; first determine, based on the respective controller status information, whether a quorum of the plurality of controllers are reachable and have a supported controller version; retrieve, in response to a positive result of the first determine, a local logical model from each of the controllers in the quorum, the local logical model, being device specific instructions from the global logical model that are specific to operability of each of the controllers of the quorum; second determine, from the retrieved local logical models of the controllers in the quorum, respective node status information for the plurality of nodes; generate, based on the respective node status information, a list of nodes to include in one or more assurance operations; and comprising at least one of an unknown health status, an unavailability status, an unknown quorum state, an unreachability from the system, or a problem authenticating the system.
 16. The non-transitory computer-readable storage medium of claim 15, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations when at least a threshold number of controllers have a particular condition comprising at least one of an unknown health status, an unavailability status, an unknown quorum state, an unreachability, or a login problem.
 17. The non-transitory computer-readable storage medium of claim 15, wherein the generating one or more assurance operations comprises stopping at least one of collection or analysis operations associated with one or more nodes when the one or more nodes have a particular condition comprising at least one of a login failure, an unreachability status, a collection failure, or an incompatible software version. 